MiniMax M3 vs Kimi K2.6: Most affordable open-source coding model

MiniMax M3 and Kimi K2.6 occupy the same useful category: cheap open-weight models, meaning their weights are available outside a closed API.

Both claim strong multi-step tool use, where the model plans, calls tools, reads results, and keeps going, at a fraction of highest-end closed-model pricing.

I want that category to work, because coding agents get expensive fast once they start looping through terminals, logs, search, and tool calls.

I had one question: which one would I actually trust to drive an agent when the task stops being a neat prompt and turns into a messy repo or a chain of SaaS actions?

And honestly you won’t get the answer from marketing pages. First, the pages show you benchmarks that nobody cares and even if the scored 80%, it would be on some easy examples it has already seen.

So, I tool a different approach. I hand-picked terminal bench questions that are provably hard even for SOTA models and a custom benchmark for real-world workflows. More on these later.

I tracked completion, cost, token use, and LLM as a judge-verified results.

MiniMax M3 vs Kimi K2.6 at a glance

• On 10 hard sandboxed Terminal-Bench coding tasks, scored pass/fail by automated tests, M3 solved 5/10 and Kimi K2.6 solved 4/10. Kimi spent about 2.4x more doing it.

• They solved the same four coding tasks. The extra M3 solve was path-tracing-reverse, a path-tracer reverse-engineering task, where M3 finished after 134 model-to-terminal round trips and Kimi timed out even with the doubled time budget.

• On the 25-task Composio tool-use suite across Gmail, Slack, Drive, GitHub, Calendar, Reddit, Notion, and web search, scored from 0 to 1, M3 averaged 0.75 and Kimi averaged 0.72. The scores were close.

• The cost gap widened on tool use: M3 cost $0.81 across the 25 Composio tasks, while Kimi cost $4.08, about 5x more.

• In my runs, M3 had the clearer edge on hard terminal coding. Everyday SaaS tool orchestration was effectively even.

For my use, M3 comes out ahead in both sets, with different margins. It wins clearly on agentic terminal coding and costs about a third per solved task. Real tool use was close enough to treat as a near tie.

Intro to MiniMax M3

MiniMax M3 is MiniMax's open-weight model aimed pretty directly at coding-agent work. It pairs a 1,048,576-token context window and cheap pricing with native tool-calling. Here, tool-calling means structured requests to run shell commands, inspect files, edit code, or call outside services.

The main spec is the context window: 1,048,576 (1M) tokens. That gives an agent room to keep much more repo state, logs, and previous attempts in view before it has to start trimming context.

On price, M3 is cheap: $0.30 per million input tokens and $1.20 per million output tokens on OpenRouter. I watch the output side closely with coding agents, since patches, retries, tool calls, and long explanations can quietly run up the bill. M3 keeps that meter low.

Intro to Kimi K2.6

Kimi K2.6 is Moonshot AI's open-weight model in this matchup. It has a 262,144-token context window and native tool-calling, and Moonshot positions it as a step up from K2.5. That made it worth testing for agent work. Tool-calling matters when the model has to request external actions, wait for results, and continue from there.

In normal terms, 262K is big, though it is smaller than MiniMax M3's 1,048,576 tokens. I still take 262K seriously, since an agent can carry a lot of repo state plus logs and tool outputs. It just has less room to be sloppy about history than M3.

Price: $0.68 per million input tokens and $3.41 per million output tokens on OpenRouter. M3 is $0.30 in and $1.20 out, so K2.6 output is about 2.8x more expensive. In agent loops, output tokens pile up fast because the model keeps explaining each move, calling tools, reading results, and trying again.

How I tested this

I ran two head-to-head tests. In each one, I kept the harness fixed and changed only the model id, minimax/minimax-m3 versus moonshotai/kimi-k2.6.

• Agentic terminal coding: Terminal-Bench, run through terminus on Daytona cloud Linux sandboxes via OpenRouter.

• Real tool use: a 25-task Composio suite, using Composio tool-router meta tools via OpenRouter.

• GPT-5.5 High as the judge

OpenRouter was the routing and billing layer for both runs.

Terminal-Bench is a benchmark of hard command-line tasks. For example, tasks can require writing a compressor or reversing a path-tracer. Each task runs inside its own isolated Linux sandbox, and the final answer is judged by an automated test for that task. In this section, “solved” means the task’s automated test passed.

The sample was the 10 hardest Terminal-Bench 2.0 tasks, so the score reflects that hard slice. It should not be read as a full-suite leaderboard score across every task.

Sandbox access goes through an agent called terminus. The model reads terminal state, chooses the next command, sees the output, then decides what to do next. I count each of those model-to-terminal round trips as a step. So when a task takes 100+ steps, that means the model stayed coherent across a long chain of shell commands, edits, test runs, and fixes.

Each Terminal-Bench task has a built-in time limit. I doubled that limit for both models with agent-timeout-multiplier 2, because an initial run at the standard limit was cutting them off while they were still making progress. At the default limit, cutoffs often happened before the agent had finished exploring a plausible path, so I used the doubled limit for the head-to-head run.

The second test was a 25-task Composio suite against real connected accounts across Gmail, Google Calendar, GitHub, Slack, Google Drive, Reddit, Notion, and web search. Composio connects agents to authenticated app actions; the tool-router meta tools let the model find and run an app action when it needs one.

The Composio tasks were graded from 0 to 1. A score of 1 means the model actually did the job inside the connected account. A score of 0 means it failed the task. Partial scores mean it got some of the required work right but missed or mangled part of it, or failed to verify the result. An independent GPT-5.5 judge model graded the finished task and checked public claims against the live web.

Related: Opus 4.8 vs GPT 5.5

Test 1: Agentic terminal coding

The run used the 10-task hard slice from Terminal-Bench 2.0, through the same terminus agent on Daytona via OpenRouter. Each task is pass/fail by automated test, and both models got the doubled time budget.

M3 won this run 5/10 to 4/10. Their four shared solves were a FEAL cipher-attack task, a code-vulnerability fix, an LLM inference-batching scheduler, and password recovery.

The extra M3 solve came from path-tracing-reverse, the path-tracer reverse-engineering task. Both models had double the normal time budget. M3 kept the agent working for 134 steps, meaning 134 model-to-terminal round trips, and eventually landed the solve. In practical terms, the agent had to keep a long chain of commands, inspection, edits, reruns, and corrections pointed at the same goal. Kimi spent $1.62 on the same task and still timed out.

The cost line is rough for Kimi. M3 finished the whole run at $2.80. Kimi spent $6.61 for one fewer solved task, about 2.4x the total spend.

The result stays narrow. The MIPS interpreter build (make-mips-interpreter), the regex-chess puzzle (regex-chess), and the two PyTorch parallelism tasks (torch-pipeline-parallelism and torch-tensor-parallelism) beat both models even with the doubled budget. My read is narrow: M3 held on slightly longer on the edge cases that were barely solvable in this run.

Test 2: Real-world tool use

This run covered 25 real jobs against connected accounts:

• summarizing a month of email by sender,

• counting a week of Slack by channel,

• organizing 1,000 Drive files,

• looking up a GitHub repo’s stats,

• scraping startups and drafting outreach emails. Each task got a 0-to-1 score from an independent grader.

• …etc

M3 averaged 0.75, while Kimi averaged 0.72. The 0.03 gap is a near tie and much closer than the coding test. Both also did well on the clean lookup-style jobs: the month-of-email summary, Notion pages, 1,000 Drive files, and GitHub repo stats all landed at 0.95 for both models.

Scores moved task by task. M3 had large leads on categorizing three years of calendar events, 0.92 to 0.15, and organizing 10 recent Drive files, 0.9 to 0.0. Kimi led on web-heavy research: Docker best practices was 0.75 to 0.35, and the microservices research doc was 0.58 to 0.25. Averaged across all 25 tasks, the suite landed close to even.

For an agent that mostly calls SaaS apps instead of grinding through shell tasks, M3 and Kimi are close enough that the $0.81 vs $4.08 total run cost becomes the practical separator.

Cost Analysis

I used the actual per-run price billed through OpenRouter and summed it per task.

Terminal-Bench cost $2.80 for M3 and $6.61 for Kimi, about 2.4x more. M3 also solved one more task. The per-solved-task math is worse for Kimi: $0.56 per solve for M3 against $1.65 for Kimi.

The tool run had a bigger spread. Across the 25 Composio tasks, M3 spent $0.81 and Kimi spent $4.08, roughly 5x. The quality scores were close, 0.75 for M3 vs 0.72 for Kimi, while Kimi cost about five times as much to get there.

OpenRouter rates explain part of that. M3 was listed at $0.30 per million input tokens and $1.20 per million output tokens. Kimi was $0.68 input and $3.41 output, so input was a little over 2x and output was about 2.8x. Kimi also tends to emit more tokens per task, so the gap compounds inside agent loops.

Caveat: these were small task counts, and OpenRouter’s billed prices can differ slightly from a vendor’s own API. The direction was still consistent on this workload: M3 was cheaper by a wide margin.

Final Verdict

My pick is M3 for both workloads, but the margins are different. The terminal-coding run is a clearer win, and M3 costs about a third per solve. The real tool-use run is a near tie.

Terminal-Bench was the cleaner M3 win: 5/10 for M3 vs 4/10 for Kimi. M3 cost $2.80 total and $0.56 per solve, while Kimi cost $6.61 total and $1.65 per solve. The task I kept coming back to was path-tracing-reverse: M3 solved it in 134 agent steps, meaning model-to-terminal round trips, while Kimi timed out even with the doubled budget.

Composio was much closer. M3 averaged 0.75 across 25 real tool tasks, Kimi averaged 0.72. Both handled the same kind of Gmail, Calendar, GitHub, Slack, Drive, Reddit, Notion, and web search orchestration well enough that I would treat them as close on that workload. M3 did it for $0.81 instead of $4.08.

When you'll notice the difference

Hard, long-horizon terminal work is where M3’s lead and cost edge matter. For SaaS orchestration, the models are close enough that M3’s lower bill is the practical separator.

Caveats: this was only 10 Terminal-Bench tasks plus 25 Composio tasks. The Terminal-Bench run used a doubled time budget, so treat it separately from standard leaderboard comparisons. Both models are still far below the best closed models on the hardest coding tasks, so I’d try them on your own workflow before swapping anything in.

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