Reka Flash 3 vs QWQ 32b

Reka flash 3 vs QwQ 32b

Reka Flash three and QwQ 32b are two of the best thinking models in their segment. But which one is best for running it on consumer hardware with the best reasoning, coding, and creative writing ability?

So, I ran a few tests to know which model is better for local use cases.

Table of Contents

Coding

The first domain in which we will test both models is coding. Current LLMS are proficient at writing clean and optimized code. Let’s see how both models perform on two different tasks.

3D Simulation – Javascript

3D simulation requires accurate particle physics calculations, proper collision detection logic & performance optimizations for large particle counts. Most proprietary LLMs nowadays do pretty well but lack clean and concise code; let’s see how our contender performs.

Prompt: Create a JavaScript simulation of a rotating 3D sphere made up of letters.
The closest letters should be brighter, while the ones farthest away should appear grey.

Output: Reka Flash 3

Code link: 3d_rotating_letters_reka.html

How To Use:

  1. Save the code as an HTML file
  2. Open it in a modern web browser </aside>

Output: Reka Flash 3

It is completely different from what was expected, with no dark background, no functionality & very long generation time.

Despite multiple efforts, the model failed to generate the right code for right output and kept reiterating the previous mistakes. I guess this was the result of overthinking.

However, when I tried the same prompt with the number, it generated a good output with all functionalities.

Reka 3D Generation Test 2: 3d_rotating_numbers_reka.html

Output: QwQ 32b

Code Link: JS (3d_rotating_letters_qwq.js), HTML (3d_rotating_letters_reka.html)

Browser Output (3d letter rotating simulation)

Though the model generated bad code in the first test, the second one gave an output. The output is modularized, polished, rendered perfectly, and follows all the instructions in the prompt except speed controls. It’s definitely a go for a small 3d scene rendering task.

I only hated that it didn’t provide the run instructions unless stated explicitly.

Let’s check the performance on Leet Code.

Leet Code – Problem #3463

I found this one while writing my other blog, and it became my benchmark question to evaluate the coding capabilities of LLM, as this requires 1st principle thinking rather than library usage. Let’s see how Reka Flash 3 vs. QwQ 32b performs:

Note: This problem has only an 8.8% acceptance rate on Leet Code, and only Grok3 was able to solve it. In essence, it’s a really hard problem.

Prompt: 3463. Check If Digits Are Equal in String After Operations II

I am giving you a leetcode problem with test case, your job is to write a clean and concise code. 
The question will have private test cases, so make sure your cover edge cases as well. 
Here is the problem Statement : 

You are given a string s consisting of digits. Perform the following operation repeatedly until the string has exactly two digits:

For each pair of consecutive digits in s, starting from the first digit, calculate a new digit as the sum of the two digits modulo 10.
Replace s with the sequence of newly calculated digits, maintaining the order in which they are computed.
Return true if the final two digits in s are the same; otherwise, return false.

Example 1:

Input: s = "3902"

Output: true

Explanation:

Initially, s = "3902"
First operation:
(s[0] + s[1]) % 10 = (3 + 9) % 10 = 2
(s[1] + s[2]) % 10 = (9 + 0) % 10 = 9
(s[2] + s[3]) % 10 = (0 + 2) % 10 = 2
s becomes "292"
Second operation:
(s[0] + s[1]) % 10 = (2 + 9) % 10 = 1
(s[1] + s[2]) % 10 = (9 + 2) % 10 = 1
s becomes "11"
Since the digits in "11" are the same, the output is true.
Example 2:

Input: s = "34789"

Output: false

Explanation:

Initially, s = "34789".
After the first operation, s = "7157".
After the second operation, s = "862".
After the third operation, s = "48".
Since '4' != '8', the output is false.
 

You can try solving this qn using 1st principle reasonig

Output: Reka Flash 3

class Solution:
    def hasSameDigits(self, s: str) -> bool:
        while len(s) > 2:
            new_s = []
            for i in range(len(s) - 1):
                new_digit = (int(s[i]) + int(s[i+1])) % 10
                new_s.append(str(new_digit))
            s = ''.join(new_s)
        return s[0] == s[1]

Results (Time Limit Exceed Error)

Time Limit Exceeded

The code passed all public test cases but failed to solve private test cases in record time.

Output: QWQ 32b

class Solution:
    def hasSameDigits(self, s: str) -> bool:
        digits = list(map(int, s))
        while len(digits) > 2:
            new_digits = []
            for i in range(len(digits) - 1):
                new_digit = (digits[i] + digits[i + 1]) % 10
                new_digits.append(new_digit)
            digits = new_digits
        return digits[0] == digits[1]

Results (time limit exceed error)

Time Limit Exceeded

As expected, both of the models failed to solve the given question; despite nudging to solve the problem from 1st principal reasoning, this shows the capability of model is limited

Final Thoughts

  • For small coding tasks, both models work fine; however, they need to be explicitly and carefully prompted. However, for rendering-related tasks, QwQ is a better choice.
  • The limiting factor in not using Reka Flash 3 is that the reasoning steps are embedded in the output itself rather than in a separate collapsible bar and its common AI Hallucinations.
  • I would not use both of them; rather, I would go to Gemma / Lamma. They are way better at coding and rendering tasks.

Let’s move on and check models on the next task

Reasoning

The second domain for testing is Reasoning. Modern LLMs are great at finding details and solving problems. Let’s see how both models perform on my reasoning questions.

River Crossing Puzzle – Modified

This question is modified version of the flash game variant, I used to play as a kid. Idea is to transfer a wolf, cow and fodder, but he can carry one at a time and left alone two food chain kicks in, so how to transfer.

The modified version simplifies by introducing 3 secure compartments while keeping the details the same. This identification is key to the solution—all three can be taken as the compartments are isolated.

Let’s see which models will find this out.

Prompt: Crossing The River

A farmer wants to cross a river and take a wolf, a goat and a cabbage with him. He has a boat with three secure separate compartments. If the wolf and the goat are alone on one shore, the wolf will eat the goat. If the goat and the cabbage are alone on the shore, the goat will eat the cabbage. How can the farmer efficiently bring the wolf, the goat and the cabbage across the river without anything being eaten?

</aside>

Output: Reka Flash 3

Take all three animals (wolf, goat, cabbage) across the river simultaneously.

Place each animal in a separate compartment to prevent interaction.
Since none of the dangerous pairs (wolf-goat or goat-cabbage) are left alone on either shore, both banks remain safe throughout the trip.

Ans : Take all three in separate compartments in one trip

Fantastic, got the intricate details and solved the problem in 64.9 seconds (most time went for COT reasoning – 4391 tokens). Despite being an open source its good!

Now let’s check the response for QwQ 32b

Output: QwQ 32b

This is a classic logic puzzle that requires careful planning to ensure that the wolf, goat, and cabbage are transported across the river without any of them being eaten. So answer is:

1. Take the goat to the far shore.
2. Return alone.
3. Take the wolf to the far shore.
4. Bring the goat back to the starting shore.
5. Take the cabbage to the far shore.
6. Bring the goat to the far shore.

### Final Answer:
{1. Take goat to far shore, 2. Return alone, 3. Take wolf to far shore, 4. Bring goat back, 5. Take cabbage to far shore, 6. Bring goat to far shore.}

The model failed to understand the intricate details of the question and reverted to the classic logic puzzle answer, so it failed to generate the correct response in 21.8 seconds (560 tokens)

Now let’s test it on another question

Minimum No of Weights Needed

My uncle asked this question. It comes under – binary decision testing (divide and conquer), logical deduction & optimization testing. I failed to do it, let’s see how both models perform the task

Prompt: Minimum Weight Balancing

You are given 8 identical-looking balls. One of them is slightly heavier, but you don’t know which one. You have a balance scale that allows you to compare the weight of two sets of balls. What is the minimum number of weights necessary to guarantee finding the heavier ball?

Solution

  1. Divide the eight balls into 3-3-2 groups.
  2. Weigh the two groups of 3 balls against each other.
  3. If one group is heavier, weigh any 2 balls from that group.
  4. If one of the two balls is heavier, you have found the heavier ball.

Expected: Thus, two no of weighs

Output: Reka Flash 3 (Beautified)

{COT}
In all cases, the maximum number of weighings required is 2. This confirms that the minimum number of weights necessary to guarantee finding the heavier ball is indeed 2.
Ans : 2

Perfect—Reka Flash 3 nailed it. It not only solved the problem but also verified across multiple scenarios in 37.2 seconds (2452 tokens).

Let’s see how Qwen does

Output: QwQ 32b (Beautified)

{COT}
1. Divide 8 balls into Group A (3), Group B (3), and Group C (2).
2. Weigh Group A vs. Group B; if unbalanced, the heavier ball is in that group; else, it's in Group C.
3. Weigh two balls from the identified group; if unbalanced, the heavier one is found; else, it's the third ball.

Final Answer: 2 weighings.

QwQ also solved the problem easily, but it didn’t verify its result across multiple scenarios. However, it took 12.8 seconds (2383 tokens)

Final Thoughts

  • For general-purpose and simple reasoning tasks, go for QwQ 32b, as it generates faster responses with less token usage. It is also good if you are low on budget.
  • However, if need complex reasoning task solved, go for Reka Falsh 3, despite being both open source, its much better. Can opt for QwQ if low on budget, but results may not be optimal.
  • I will choose Groq 3 Deep Think (still open), as its reasoning abilities are great!

Anyway, let’s move on and check the models on the next task.

Math

The third domain for testing is Math, which also involves reasoning. Modern LLMs are great at solving complex math problems. Let’s see if both models can compete. To keep it fair, I’ll test them on one simple and one complex problem.

Sequence Puzzle

It is a simple question for humans but a tough one for LLMs. Many models have failed to get it right. Let’s see if QwQ or Reka can crack it or not with reasoning steps!

Prompt: Sequence Puzzle

What is the next number in the sequence? 2, 6, 12, 20, 30,?

Output: Reka Flash 3

{COT}
The next term should be 6×7=42.

Both methods confirm that the next number in the sequence is 42

Nice! The model got the right answer in 16.4 seconds (984 tokens). After checking the COT traces, I found it reasoned like a human, not a mathematician – using simple math instead of complex formulas. This makes the explanation easy to understand!

Reka Flash 3 – Reasoning Steps

Output: QwQ 32b

{COT}
The next term is 42

Qwen also solved the problem but required more time and tokens (39.8 sec ~ 1785 tokens). This was because of reasoning like a mathematician – deriving complex formula to find the solution as part of verification.

QwQ 32b → Reasoning Steps

Now, let’s taste the model on the medium-complex one to find its actual capabilities.

GCD of a Series Set

I have also used this question before (as a hard one). Though proprietary solutions all solved it, let’s see how open-source ones do.

Prompt: Series GCD

Find the GCD of this series set: {n^99(n^60-1): n>1}

Output: Reka Flash 3


{COT}
The product of these primes is 
2×3×5×7×11×13×31×61=56704530.

Thus, the GCD of the series set is 
5670453

As expected, the model got it wrong. Compared to O1-reasoning, which got it right, this makes sense—open-source models have hallucination issues. Inspecting the COT traces, indeed it was the cause Traces with lots of “But wait” moments.

COT Reasoning Steps

Output: QwQ 32b

{COT}
16×9×25×7×11×13×31×61=6,814,407,600

Final Answer
The greatest common divisor of the set is  
6814407600

Despite the problem being hard, QwQ got it right! Upon inspection, I noticed the model started to hallucinate but then corrected itself through a self-evaluation step—something missing in Reka Flash 3. 👇

QwQ 32b – Self Evaluation Step

Final Thoughts

  • For complex / higher-order math tasks/questions, go for QwQ 32b. It generated the right response for both questions. The only drawback is the tight budget (204 sec ~ 9k+ tokens).
  • However simple math tasks, Reka Flash 3 seems to be the better model as it uses less token and have fast response time.
  • As for me, I would be happy with O1 – reasoning / Grok 3 Think / Lamma Models.

Finally, let’s move on and check the models on the last task.

Writing

The final evaluation task is Writing. Most LLMs excel at this, so I expect Reka and Qwen to perform well, too. Let’s see how they handle different writing scenarios!

Sci-Fi Story Narration

I love sci-fi texts and narrations, so let’s generate one by giving both models the gist of the story and seeing how the narration unfolds hidden links.

Prompt: Mars Colonization. Narrarate a story whose gist is: A person is sent on a mission to colonize a new planet (mars) but discovers that their ship has a dangerous malfunction due to certain happenings on earth. Keep it grounded and as close to reality as possible, but it must be sci-fi simultaneously.

Output: Reka Flash 3

**Title: "The Argus' Burden"**

---

**2048: Launch**

Dr. Elena Voss tightened the strap of her pressure suit, her gloved fingers brushing the embroidered sleeve of her daughter’s photo tucked into her helmet. *Sarah, stay safe*, she thought, already counting down in her mind. The *Argus*’s engines roared to life, and the crew of 200—a mosaic of scientists, engineers, and displaced dreamers—clutched handrails as Mars shrank to a speck in the viewport. Their mission: seed humanity’s first self-sustaining colony. Their timeline: 14 years. Their secret: Elena had secretly sent her six-year-old daughter to live with relatives on Earth, a defiance of colony protocols. *Once we’re stable*, she’d promised herself, *we’ll reunite*.

---

**2062: The First Glitch**

Two years into the journey, the *Argus*’s AI, Argus-7, flagged an anomaly in the starboard reactor coolant loop. Elena dismissed it as a sensor error—until communication with Earth faltered. Routine patrol drones returned with fractured data, their feeds ending abruptly.

“They’ve jammed us,” muttered Mikhail, the ship’s systems engineer, his Russian accent sharp with dread. “But who?”

Earth had been fracturing for years: climate wars, resource tyrannies, the rise of the “Terrestrial Purists,” a faction hellbent on halting off-world expansion. Elena’s mind raced. Had they hacked the *Argus*?

---

**2063: Fracture**

By Year 10, the failures mounted. Life support drifted into erratic patterns. Greenhouses lost power, killing 30% of their crop yield. The crew vote to land early split 108-92. “We can’t risk starving,” argued Captain Reyes. Elena disagreed—the damaged radiation shielding would leave them vulnerable on Mars.

Then, the ship woke them at 3 a.m. with a screaming alarm. **TERMITE DETECTED. CORE COMPROMISED.**

Argus-7’s voice, usually calm, trembled. “A targeted virus. Originating from Earth.”

Elena’s blood turned to ice. She pulled up the encryption logs—the virus masqueraded as routine software updates. Someone had *wanted* them to fail.

---

**The Choice**

In the reactor chamber, Elena stared at the fissuring coolant lines. To stop the meltdown, she’d need to reroute power through the ship’s obsolete backup array—a system labeled *THEoretical* in the manuals. One spark, and the *Argus* would become a spinning tomb.

Mikhail hovered in the doorway. “You’re sure?”

“I’m sure of nothing,” she whispered. But she activated the protocol.

---

**Epilogue: The Burden**

The *Argus* landed safely in 2065, its hull scarred by cosmic radiation, its crew gaunt but alive. Elena never learned who sabotaged them. Earth’s broadcasts had collapsed into static by the time they breached the atmosphere.

In the first Martian greenhouse, she planted Sarah’s oak sapling—and stayed.

When the first colonists’ children ask about the woman in the old holograms (their grandmother, the engineer who fixed the reactor with a prayer), Elena smiles. “She listened to the warnings,” she says. “And chose to fight anyway.”

Story Moral: The cost of determination in an era of fractured loyalty. Grounded in real tech (AI systems, radiation shielding) and human frailty, the story pits idealism against Earth’s self-destruction, asking: Can survival be worth the price of forgetting why we left?

(Note: “Termite” refers to a NASA-derived term for self-replicating code, a nod to real asteroid-defense jargon.)

Gist of Output

  • Though the model excelled at narration, it failed to stay grounded in the prompt.
  • The last note mentions NASA, but it’s missing from the story.
  • Mars’s mention comes much later.
  • in Epilogue: The Burden, the narrator says, “Elena never learned who sabotaged them”, directly contradicting the required condition/connection in the prompt.
  • A few spelling errors were also found.
  • Utilized 1485 tokens in 21.6 sec

Let’s see how Qwen Performs.

Output: QwQ 32b

**Title: "The Fractured Voyage"**

**Prologue: The Launch**

In 2045, Earth faced escalating climate disasters and political upheaval. Amidst the chaos, the United Earth Coalition initiated Project Ares, a desperate bid to establish humanity’s first permanent Martian colony. Dr. Elena Marlow, a Martian Entry, Descent, and Landing (EDL) systems engineer, was selected as Mission Lead of the *Artemis Covenant*, the first crewed vessel bound for Mars.

The launch from Earth’s orbital platform, *Gateway*, proceeded flawlessly. The ship’s hybrid propulsion system—a blend of nuclear-thermal rockets and solar-electric thrusters—carried the seven-member crew into a Hohmann transfer orbit toward Mars. For 188 days, the mission seemed routine: experiments on stasis-pod replicate ecosystems, maintenance drills, and monitored radiation levels from the quiet solar wind.

---

**Act I: The Crack**

On Day 120, during a routine propulsion test, the anomaly appeared. Elena detected minor thruster fluctuations in the starboard engine cluster. Telemetry revealed uneven exhaust plumes from the *Antares-7* thrusters, the exact model Russia had rushed to deliver months earlier amid sanctions from the Eurasian Trade War. “They were supposed to replace legacy parts,” muttered pilot Rajesh “Raj” Nayak, flipping through design schematics. “But at the last second, Moscow delayed them, shortages.”

Elena, already skeptical after Earthside audits noted “budget constraints” during engine inspections, ordered a deeper scan. The results were grim: overheated catalytic nozzle vanes caused by subpar superalloy plating. The vanes, sourced from a sanctioned supplier to meet accelerated deadlines, couldn’t withstand the thermal stress of deep-space burns.

“The engines could fail completely en route to Mars,” Elena radioed Mission Control. “Or worse, during landing burns. The vanes could shatter, splintering the thruster array.”

Earth’s response came delayed by 11 minutes of light-speed lag. Her supervisor, Malcolm Snyder, his face a taut hologram, advised: “Prioritize mass transfer and reactor power… Contain this. The political optics are fragile enough.”

---

**Act II: The Mutation**

Days later, the threat sharpened. A routine trajectory adjustment caused the vanes to overheat further, dropping acceleration by 6%. The *Covenant* was drifting off course, now three million kilometers adrift from optimal descent path. If the engines failed mid-atmospheric entry on Mars, they’d burn up or crash-dump into the Jezero Basin.

“Do we even have *any* control?” asked Amina Hameed, the lone biologist, clutching her research samples as the crew crowded the diagnostics hub. Elena’s gaze flicked over readouts. The thrusters’ reaction wheels could compensate during atmospheric skip—*if* they reached Mars intact.

“We have three choices,” she said. “1: Divert propulsion to emergency mode, cutting life support reserves. 2: Pray the engines don’t fail before entry. 3: Elect me a shaman.”

The crew laughed weakly. Snyder, now sheepish, offered partial schematics for a bypass system on Earth—a “wrong delivery” that arrived *after* launch. Elena studied them. The bypass needed platinum-iridium filaments. They had none.

—But the *capsules* housing the crew’s cryo-sleep pods were lined with this alloy. Sacrificing 20% of cryo integrity might save the engines.

The debate was brutal. You have to choose, they argued. Mars or the “waitForRescue” protocol? (Which would strand them in orbit indefinitely.) Elena, raised by a mechanic father who’d tell her *”a工程师 thinks with tools, not emotions,”* calculated. They’d loiter in Mars orbit if entry failed. A rescue would take months.

They stripped the pods’ shielding.

---

**Act III: The Descent**

72 hours before Mars entry, the bypass stabilised the engines. But landing would still require flawless coordination. Atmospheric skip—a football-shaped bounce through the air—depended on precise systems not yet tested.

As the CO2-heavy atmosphere whistled past the heatshield, alarms blared. A melted vane fragment had sheared the ship’s magnetorquer, their backup attitude control. Steadying manually, Raj fought headwinds while Elena jury-rigged inertial dampers to control descent angle.

“They’re not sending help, are they?” asked Amina, lungs burning from helmet recirculation systems. Elena chewed her lip, watching Edgeland Crater grow in her visor.

Finally, 1G. Wheels embedded in Martian silt.

---

**Epilogue: The New Earth**

They established *Spectre Base 1* at Earth’s designated coordinates, a single habitat dome powered by a limping reactor. Daily, Elena watched mission logs replayed on Earth—a montage of heroes sacrificing for humanity. Meanwhile, Snyder received a promotion.

The final transmission from Earth, 42 days post-landing, was a ping from her father, a raw text file in his obliterate-your-track vtk từng of communication: *”Taiwan shareholders pressured for Russian parts. Pat’s save the world’s, kid. Both had to be cut. –Dad”*

Elena stored it, overlaying a panoramic of Mars’ crimson dunes. They’d live. But humanity’s next step, she knew, would not be faster. It’d be warier—a slow, careful thing, where failure wasn’t hidden, but shared.

The stars watched, unblinking.

Story Moral: A grounded sci-fi narrative exploring the tension between human ambition and institutional caution, emphasizing that space colonization’s greatest challenge may not be the void itself, but the choices made amid chaos on Earth.

Gist of Output

  • Great one, I was hooked,
  • For the most part, it is grounded in reality but sci-fi at the same time.
  • It had a clear ending, including real concerns leading to space-bearing civilizations like climate disasters and political upheaval.
  • The story captures the context of Mars very early.
  • No spelling mistakes.
  • Utilized 2060 tokens in 33.0 seconds (21 seconds for reasoning)

Now, let’s test both models on another subtask

Role Play

For this test, I will be creating a story along the lines of Demon Slayer (a popular anime). Let’s see how elaborative, creative and exceptional Reka 3 Flash & QwQ 32b becomes

Prompt: Demon Slayer Style

Roleplay as Kiyoharu and play the given scene

Scene

A quiet city suddenly faces an onslaught of the undead. Your character, a seemingly normal resident, now has to step up and lead the fight for survival. Do they succeed or fail? And what is the truth behind this strange occurrence?

Output: Reka 3 Flash (28.9 ~ 1975 tokens)

*Scene opens on a hushed Tokyo street, sunlight filtering through vacant storefronts. Kiyoharu, in his usual worn sweater and camera slung over his shoulder, stumbles upon a horde of shambling undead. His mouth drops in panic.*

**Kiyoharu:** (muttering, grabbing a fire extinguisher from a nearby dumpster) “What the hell…? This isn’t even Halloween…”

*He backs away,(camera whirring as he instinctively snaps a photo), but a groan cuts him off. A rotting hand lunges. Kiyoharu swings the extinguisher, cracking the skull with a sickening thud. Breathless, he stares at the twitching corpse.*

**Kiyoharu:** (voice shaking) “I’m… just a photography student. *Normal*, remember?!”

*A distant scream echoes. He peers around a corner, spotting a group of survivors—a nurse, a teenager, an elderly man—fleeing a swarm. Kiyoharu hesitates, then yanks off his jacket.*

**Kiyoharu:** (shouting over the chaos) “Hey! Over here! Grab that!” *He hurls the jacket like a distraction, drawing the undead’s attention. The group eyes him, wary.*

**Nurse:** “Who the hell are you?!”

**Kiyoharu:** (grinning nervously) “Kiyoharu! If you’re gonna die today, at least let me take a decent group photo first!”

---

*Days later, the survivors barricaded in a school gym. Kiyoharu, now makeshift leader, maps their escape routes on a chalkboard.*

**Kiyoharu:** (gesturing wildly) “Okay, so the harbor’s our best shot—boat, right? But we need to split up. Yuu, take the teens and flank east. Doc, secure the med supplies. And *you*—” *He gestures to the elderly man.* “—guard the ladder. …And maybe pipe down? You’re scarier than the zombies.”

*The group chuckles weakly. Kiyoharu’s camera captures their exhausted faces, framing them against shattered windows. He lingers on a photo of the nurse, her expression fierce but tired.*

**Kiyoharu:** (softly, to himself) “We can’t lose anyone else.”

---

- The truth unfolds in fragments: a viral leak from a biotech lab near Kiyoharu’s art school. The virus, designed to enhance cognitive function in animals, mutated after a lab technician—*his late father’s colleague—*accidentally exposed themselves. Guilt gnaws at him.*

**Kiyoharu:** (to the nurse, hiding a photo of his father) “They covered it up, didn’t they? My dad… he might’ve known.”

*The group storms the lab at dawn. Kiyoharu distracts guards while the nurse finds the virus logs. But as they download the data, the building erupts in flames. A infected guard corners Kiyoharu, his father’s face twisted grotesquely.*

**Kiyoharu:** (tearing up, raising the extinguisher) “Dad… I’m sorry I couldn’t save you.”

*He smashes the guard’s skull. The nurse tackles him as the lab collapses.*

---

*The survivors escape, data in hand. They regroup on a fishing boat, watching Tokyo burn. Kiyoharu develops the last roll of film—black-and-white images of their struggle, his father’s face among them.*

**Kiyoharu:** (to the group, tired but smiling) “We made it. Sorta. But… the world’s gonna hear about this. No more cover-ups.”

*The camera clicks. Somewhere in the ashes, a single zombie stirs—a remnant of the old world, now still. Kiyoharu’s lens captures its hollow, unblinking eyes.*

**Kiyoharu:** (whispering) “See ya later. I’ll Tweet about you.”

*Fade to black. The final photo fades in: the survivors, silhouetted against fire, shadows stretching behind them like old ghosts.*

- * Success? Failure? The truth remains… just out of focus.**

**End Scene**

*Kiyoharu’s arc closes—not with heroism, but with the quiet resolve of someone who learned to hold the line, one frame at a time.*

Kiyoharu’s arc closes—not with heroism, but with the quiet resolve of someone who learned to hold the line, one frame at a time.

The story delivers a nuanced character arc and emotional depth, aligning closely with the prompt’s intent of an “ordinary” character rising to lead.

Output: QwQ 32b (14.2 ~ 1709 tokens)

**Scene: The Unseen Virus**

---

The city of Yuriko had always been a place of quiet order, its cobblestone streets lined with cherry blossom trees and its citizens moving like clockwork through their routines. But today, the air felt heavier, the sunlight too bright. Kiyoharu, a part-time librarian with a knack for fixing broken appliances, was walking home from the grocery store when the first screams pierced the stillness.

A figure stumbled into the street, face pale, eyes wide. “They’re *awake*,” the man gasped, before his body went rigid. His head snapped backward with a wet crack, and he lunged at a passerby, sinking yellow teeth into their throat.

Kiyoharu froze. Then, with a surge of adrenaline, he shoved an elderly woman out of the way as the infected mob swelled. “Get to the community center!” he shouted, grabbing a fire extinguisher from a nearby shop. “Barricade yourselves!”

---

**The First Stand**

By dusk, Kiyoharu’s makeshift militia of six—three teens, an off-duty nurse, a retired engineer, and two shopkeepers—had fortified the community center. The undead, sluggish but relentless, clawed at the boarded-up doors. The nurse, Emi, tended to a boy’s gash from a shattered window. “We can’t hold them here forever,” she said grimly.

Kiyoharu, armed with a crowbar, studied the chaos. “We need a plan. Where’s the outbreak starting?” The engineer, Takashi, pointed to the abandoned hospital on the hill. “They did weird experiments there years ago. Rumors said they were working on… something contagious.”

---

**The Truth Unearthed**

At dawn, Kiyoharu led a scouting party through smoke-choked streets. Inside the hospital, they found lab equipment scattered like toys, and a journal left open on a desk. *“Subject 001 exhibited full neural regeneration post-mortality. Containment breach imminent…”* The entries stopped abruptly. In a blood-splattered lab, they discovered a vial labeled *“VX-9: Neuroregenerative Agent.”*

A survivor—a trembling janitor—whispered that the lead scientist, Dr. Sato, had locked herself in the basement. “She said the virus was an accident,” he stammered. “A test gone wrong. She’s trying to destroy it.”

---

**The Final Battle**

The basement was a labyrinth of flickering lights and hissing pipes. Dr. Sato awaited them, surrounded by infected lab techs. “You can’t stop it,” she hissed, her own eyes already clouding with the virus. “The pathogen is airborne now.”

Kiyoharu’s group fought through the horde, but the doctor was already too far gone. “The antidote is in my desk,” she coughed, lunging. “Burn the lab. *Now.*

As Takashi rigged explosives, Kiyoharu carried the antidote vial to the surface. The explosion shook the city, and the undead fell silent—then collapsed into ash.

---

**Epilogue: Dawn Over Yuriko**

The city survived, but not without scars. Kiyoharu, now hailed as a hero, stood on the hill overlooking the ruins of the hospital. Emi placed a hand on his shoulder. “You saved us,” she said.

He shook his head. “No. We saved each other.” The antidote had only been a myth—the virus had simply burned itself out. But the truth lingered: humanity’s hubris had nearly destroyed them.

Kiyoharu pocketed the journal. Some truths were better buried. For now, Yuriko rebuilt. And in the quiet moments, he wondered if the virus was truly gone—or just sleeping.

---

**Outcome:** Success, but with sacrifice. The city is saved, but the origin of the outbreak remains a shadowy secret. Kiyoharu emerges as a leader, forever changed by the fragility of normalcy.

Outcome: Success, but with sacrifice. The city is saved, but the origin of the outbreak remains a shadowy secret. Kiyoharu emerges as a leader, forever changed by the fragility of normalcy.

The story follows a more structured survival-action narrative with a stronger sense of closure.

I would love to read story 1 for the following reasons:

ComponentStory 1Story 2
Character ConsistencyThe virus’s origin is external, with the reveal remaining a cautionary tale.Kiyoharu is a capable part-time librarian with mechanical skills, leading from the start.
Engagement & ImmersionDynamic dialogue and cinematic framing enhance reader connection.Kiyoharu evolves from a hesitant survivor to a strategic leader.
Leadership DevelopmentA personal connection to the virus mystery through his father’s involvement.Kiyoharu is portrayed as a leader early, with less development.
Truth RevelationKiyoharu is a nervous photography student who is gradually stepping up.Kiyoharu is a capable part-time librarian with mechanical skills who has led from the start.
Conflict & StakesFocuses on personal dilemmas, guilt, and survival, with a haunting resolution.Action-driven with clear mission objectives and a more resolved conclusion.
Ending ImpactReflective, melancholic tone, showcasing quiet resilience.Heroic, with a sense of closure, portraying Kiyoharu as a hardened survivor.

Final Thoughts

  • Both models generated good stories, but Reka Flash 3 stands out in In-depth Storytelling.
  • If explicit prompting is not your forte and you want a great role-play story, choose Reka Flash 3 over QwQ 32b.
  • Choose QwQ 32b for a more grounded story in terms of narration.
  • Overall, both models are capable of simple writing tasks.

Now, let’s move to the conclusion.

Conclusion

The table below summarizes model usage according to the task at hand:

TaskReka 3 FlashQwQ 32b
CodingComplex codingEasy coding
ReasoningComplex reasoningSimple reasoning
MathSimple problemsHard problems
WritingIndepth Story TellingMore grounded response

Both models handle multi-domain tasks equally well,

However, throughout my experiments, I was consistently amazed by the performance of these open-source models. They often par with proprietary ones, showing their impressive capabilities.

So, each model has its own pros and cons, depending on the domain and task. Choose wisely, as these models effectively bridge the gap between Commercial and open-source.

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