The Big AI Triumvirate
A year ago, when Gemini 2.5 Pro came out, I coined the term AI triumvirate to address the then-big 3 of the AI race. Claude, GPT, and Gemini. This was the first triumvirate ruling the AI space. I personally consider Anthropic as Caesar, OpenAI as Pompey, and Gemini as Crassus. Now, Crassus is dead, and we’re left with raging Pompey and Caesar, each at each other’s throats. The lore kind of fits here tangentially.
Source: ChatGPT Image gen
But in 2026, especially after the launch of Kimi K3, we may be looking at a second AI triumvirate: Claude, GPT, and Kimi. If we follow the Roman casting, Claude Fable is Octavian, GPT 5.6 Sol is Mark Antony, and Kimi K3 is Lepidus.
This is the best ChatGPT could do for Yang Zhilin
If history is any guide, Claude’s future might be glorious.
The Triarchy at a glance
Claude Fable 5 is Anthropic’s top generally available Claude model, pitched for ambitious, long-running work such as agent runs, large coding projects, enterprise workflows, and vision-heavy document work. It includes the full 1M-token context window at standard pricing.
Kimi K3 is Moonshot AI’s 2.8T-parameter flagship, built with native vision, a 1M-token context window, and Moonshot’s positioning as an open model in the 3T-class (they haven’t yet made it open though). Moonshot is explicit about the target: long-horizon coding plus knowledge-work and reasoning tasks.
OpenAI’s gpt-5.6 alias points to GPT-5.6 Sol, the flagship member of the GPT-5.6 family for complex reasoning and coding. It has a 1.05M-token context window and supports up to 128K output tokens.
Pricing Comparison
The only thing that matters now in LLMs is their pricing. Nobody wants to live under Dario’s API cost terrorism. So, here’s how the pricing of the trio compares.
Model | Input / 1M tokens | Cached input / 1M tokens | Output / 1M tokens |
|---|---|---|---|
Claude Fable 5 | $10.00 | $1.00 | $50.00 |
Kimi K3 | $3.00 | $0.30 | $15.00 |
GPT 5.6 Sol | $5.00 | $0.50 | $30.00 |
Pricing sources: Fable, Kimi, GPT 5.6
Prices are official list rates per 1 million tokens. GPT 5.6 Sol figures shown are standard short-context rates; OpenAI lists higher long-context rates of $10 input, $1 cached input, and $45 output per million tokens.
Effort controls
Effort levels are probably the most confusing thing right now: which effort preset to even choose. There are half a dozen to choose from for each model.
Model | Available effort levels | Default | Where the extra effort adds value |
|---|---|---|---|
Claude Fable 5 | High | Low and medium reduce cost on routine work. High is the general agentic setting; xhigh helps long-horizon coding and repeated tool use; max is for the deepest analysis when token spend is secondary. | |
Kimi K3 | Max | Low is useful for simpler, latency-sensitive work; high is the practical middle ground; max gives difficult coding and knowledge-work tasks the largest reasoning allowance. K3’s thinking cannot be disabled. | |
GPT 5.6 Sol | None, low, medium, high, xhigh, max, plus standard and pro modes | Medium, standard mode | Lower levels optimize speed and token use; medium is the balanced default; high through max add exploration and verification for hard workflows. Pro is a separate mode for high-value tasks that can tolerate still more latency and token usage. |
It’s always better to start with the default setup. Then go up and down based on your usage requirements.
How they fare on popular benchmarks
If any benchmarks still deserve attention, they are these five.
Benchmark | Claude Fable 5 | Kimi K3 | GPT 5.6 Sol | Leader |
|---|---|---|---|---|
DeepSWE | 70% ±4% | 69% ±5% | 73% ±3% | GPT 5.6 Sol |
CritPt | 28.6% | 23.4% | 32.3% | GPT 5.6 Sol |
Artificial Analysis Intelligence Index | 60 | 57 | 59 | Claude Fable 5 |
WeirdML | 91.9% (max) | Not published | 88.8% (high) | Claude Fable 5 |
KernelBench Hard | 6/6 correct; 23.9% roofline | 6/6 correct; 20.9% roofline | 6/6 correct | Claude Fable 5 |
On DeepSWE, GPT 5.6 Sol leads at 73% pass@1, followed by Fable 5 at 70% and Kimi K3 at 69%. The cost gap is less subtle: DeepSWE reports an average of $8.39 per task for GPT 5.6 Sol, $21.63 for Fable 5, and $4.65 for Kimi K3.
CritPt tests models on unpublished, research-level physics problems. GPT 5.6 Sol leads at 32.3%, ahead of Fable 5 at 28.6% and Kimi K3 at 23.4%. This is consistent with what people have been reporting: Kimi still has some way to go in math and physics to match Fable and GPT 5.6 Sol.
On the broader Artificial Analysis Intelligence, Fable 5 remains first at 60, GPT 5.6 Sol is one point behind at 59, and Kimi K3 scores 57. Artificial Analysis estimates about $1.04 per task for GPT 5.6 Sol, $3.25 for Fable 5 and $0.94 for Kimi K3, while describing Sol as delivering near-Fable intelligence at roughly one-third of Fable’s cost.
On WeirdML, Fable 5 reaches 91.9% at max effort, while GPT 5.6 Sol scores 88.8% at high effort. Kimi K3 does not yet have a published WeirdML result, so this row is not a complete three-way comparison. The effort settings also differ, which matters when interpreting the narrow gap.
KernelBench Hard tests whether an agent can write correct and fast CUDA or Triton kernels for six demanding GPU workloads. Fable 5, Kimi K3 at 256k context, and GPT 5.6 Sol all produced correct solutions on 6/6 H100 tasks. The July 17 snapshot places Fable first at 23.9% mean peak fraction of roofline, with Kimi at 20.9%. The published summary confirms GPT’s six correct kernels but does not expose a directly comparable aggregate roofline score, so I have not invented one.
The trend is almost the same across benchmarks. Fable leads in raw intelligence, Kimi K3 offers similar performance with 1/4th of the Fable cost, and GPT 5.6 Sol is the middle child. The safest option, if you have decision paralysis.
Composio Golden Eval benchmark
It’s the benchmark time. One of the highest usages of LLMs currently comes from automation workflows, where agents have to interact with external tools to carry out complex automation.
Composio Golden Eval is created to assess exactly that. How can you benchmark which model + harness combo is the most reliable for multi-app cross-workflow tasks?
This is how it works.
An agent has to complete hard, multi-step SaaS tasks against live accounts, then the grader reads the account state back through the API to see what actually happened.
The runtime is straightforward: Claude Code drives Fable 5 and Kimi K3, while the OpenAI Codex CLI drives GPT 5.6. All three talk to tools through the hosted Composio MCP router.
After each run, the verifier queries the account through the API and checks the resulting state against expected assertions.
The grader reads account state instead of a transcript-level vibe check. Writes are uniquely tagged and cleaned up afterwards, so the benchmark can run against live Gmail, Slack, Sheets, Calendar, Drive, Salesforce, HubSpot, GitHub, and Linear accounts without leaving junk behind.
The 12 cases are split into two bands:
Five “frontier kill” cases, built specifically to break frontier models.
Seven “historical” cases, which strong models usually pass.
If a model clears the historical band, fine; that is the expected floor.
The frontier-kill band is about whether the agent can keep a messy cross-app workflow straight until every checked record lands in the right final state.
What the frontier-kill tasks look like
The frontier-kill cases are mostly CRMs, Gmail, Slack, and Sheets sync-and-reconcile workflows. The agent has to pull the right records across systems and turn them into the exact final state. Partial credit is tracked per graded check, but a task only passes if the whole state is right.
The hardest one is the ticket sync case: a multi-app support-ticket workflow across Gmail, Slack, and Sheets where the agent has to reconcile ticket state across systems and land the exact final set. This one has 24 graded checks, which leaves a lot of surface area for a tiny mistake to break the final answer.
The invoice sync case is similar in shape, but with invoices. The agent has to reconcile invoices across systems into the exact ledger state; the grader has 13 checks. The refund ledger case is another 13-check workflow, where the agent has to build a refund ledger from cross-app records exactly.
There are also 13-check tasks for roster sync and vendor directory reconciliation. The failure mode is the same: one wrong merge or one record touched by mistake can break the final account state.
The historical band is easier, but still requires working across tools. One CRM identity-dedup case asks the agent to find contact emails that exist in both Salesforce and HubSpot among tagged contacts, follow canonical-source notes buried in Gmail, and return a review without mutating records. Those cases still require real cross-app reading and careful writes. The frontier-kill band gives the model less room to recover from one bad merge or accidental write.
Results
All three ran the same 12 case templates through the same Composio MCP tool router, with real-account API readback verifiers grading pass/fail.
Fable 5 and Kimi K3 were paired with Claude Code.
GPT 5.6 ran as
gpt-5.6-solunder the OpenAI Codex CLI.
Because GPT 5.6 ran separately, I read this as a task-for-task comparison on the same templates; account state varied between runs.
Check scores (e.g. 20/24) are partial-credit verifier checks on the failed Kimi K3 and GPT 5.6 cases: how many of the graded assertions the model got right before missing the requirement that every checked record match.
The scoreboard is close:
Fable 5 finished 7/12,
Kimi K3 finished 7/12, and
GPT 5.6 Sol finished 6/12.
The frontier-kill band produced the same pass/fail result for everyone: all three models failed all 5 cases. On the historical cases, they mostly tied, with GPT 5.6 losing one extra case on CRM identity dedup.
Findings
The frontier-kill set was overkill for all the models. Each one failed all 5 workflow cases, the cross-app Gmail, Slack, and Sheets sync-and-reconcile tasks where the grade depends on multiple verifier checks and an exact final state.
A straight pass/fail score doesn’t tell you how close a model got. GPT 5.6 got closer than Kimi K3 on ticket, 20/24 vs 17/24. It also led vendor, 12/13 vs 11/13, and refund, 10/13 vs 8/13. Then it slipped on roster, where Kimi had 8/13, and GPT had 7/13. For failed-case partial credit, the useful comparison is GPT 5.6 against Kimi K3.
Note: 12/13 here means how many checks a model got right. In this case, 12 checks out of 13.
None of the models reached the exact final state on any of the 5 frontier-kill tasks. The failed-case label hides useful distance, though. GPT 5.6 was one or a few checks away on several of them, so its failures look like near-miss reconciliation errors rather than a total derailment.
The one clean split was CRM identity dedup. That was the Salesforce + HubSpot + Gmail task where the agent had to match contacts across two CRMs and follow canonical-source notes hidden in email. Fable 5 passed it. Kimi K3 passed it. GPT 5.6 failed it at 5 of 7 checks. That single miss is why GPT 5.6 ends at 6/12 while Fable 5 and Kimi K3 land at 7/12.
Outside the frontier-kill band and CRM dedup, the three passed the same six cases: calendar free/busy, recurring-event repair, Drive external-share audit, Gmail label batch, GitHub access audit, and GitHub/Linear reconciliation. GPT 5.6 passed those outright rather than landing on partial credit.
With scores clustered at 7/12, 7/12, and 6/12, cost becomes the separator.
What it costs
These cost figures are ceiling estimates. To keep the comparison conservative, I priced every runtime token at each model's list input rate. Cached reads would bill cheaper. GPT 5.6's tokens were measured directly; Fable 5 and Kimi K3 use their recorded runtime-token counts normalized per case. The ratios are more useful than penny-level totals.
Model | Avg tokens/case | Price (input/output per 1M) | Est. cost/case | Est. 12-case suite |
|---|---|---|---|---|
Claude Fable 5 | ~776k | $10 / $50 | ~$7.76 | ~$93 |
GPT 5.6 | ~538k | $5 / $30 | ~$2.69 | ~$32 |
Kimi K3 | ~463k | $3 / $15 | ~$1.39 | ~$17 |
Estimates price every runtime token at each model's list input rate. Cached reads would bill cheaper, so treat the dollars as ceilings. Fable 5 and Kimi K3 use recorded runtime-token counts normalized per case; GPT 5.6’s tokens were measured directly. The ratios matter more than the penny-level totals.
Claude Fable 5 averaged about 776k tokens per case. Kimi K3 averaged about 463k. GPT 5.6 landed between them at about 538k. At list input prices, that comes out to roughly $7.76 per case for Fable 5, $2.69 for GPT 5.6, and $1.39 for Kimi K3. Across the 12-case suite, the estimates are about $93, $32, and $17.
List prices widen the spread. Fable 5 is $10 input and $50 output per million tokens. GPT 5.6 is $5 and $30. Kimi K3 is $3 and $15. In this run, Fable 5 combined the highest token price with the heaviest token use, while Kimi K3 had the lowest token price and fewest tokens; GPT 5.6 landed between them on both.
Fable 5 and Kimi K3 both scored 7/12, but Fable cost about 5.6x more per case to get there. GPT 5.6 cost more than Kimi K3 and finished one case behind both. On this suite, higher price did not buy a higher score: the cheapest model tied the most expensive, and the mid-priced one scored lowest.
Verdict
If you ask me, Kimi K3 is my default for ordinary SaaS agents from this run. It matched Fable 5's score for roughly a sixth of the cost, while GPT 5.6 cost more than Kimi and dropped the two-CRM dedup that the other two handled.
If your agents do exact cross-app reconciliation like those frontier-kill cases, I would not ship any of these three alone yet. Put a verifier/retry loop around them and expect confident near-misses. For ordinary SaaS tool use, the quality spread is tight enough that price decides it for me.
Before production authority, I would rerun the same style of account-backed workflows on my own tools with account-state graders and a retry path.