Last verified: August 30, 2026. The test results on this page were produced by MiniMax-AI.chat on the dates stated in each section.
There is no single best AI for coding, and any page that gives you one name without telling you how it was measured is guessing. What exists is a set of measurable differences that matter for specific work: whether a model finishes a task autonomously, whether it writes files or stalls while reasoning, how it handles a large repository, and what it costs per resolved task.
This page does two things. It publishes the full methodology and results of the coding tests we ran ourselves, including the failures. And it gives you a repeatable protocol you can run in about thirty minutes to test any coding model against your codebase, which is the only benchmark that decides the question for you.
Independent-site notice: MiniMax-AI.chat is an independent educational website. It is not MiniMax’s official website and is not endorsed by or affiliated with MiniMax. Product names and trademarks belong to their respective owners. We publish our own test methodology and results; figures published by model vendors are their own and are not comparable with ours.
What “best for coding” actually means
The phrase hides four different questions, and models that win one frequently lose another. Decide which one you are asking before you compare anything.
| Question | What it measures | Why it decides your choice |
|---|---|---|
| Can it finish the task alone? | Autonomous resolution rate | A model that needs three corrections per bug is slower than writing it yourself |
| Does it work in my repository? | Context handling on real code | Performance on isolated puzzles does not transfer to a 40-file project |
| Can it call my tools? | Tool and function calling reliability | Agentic workflows fail entirely if tool calls are malformed |
| What does a fix cost? | Price per resolved task, not per token | A cheap model that fails half the time costs more than an expensive one that does not |
What we tested, and what happened
On July 28, 2026 we ran a bug-fixing benchmark against ten original repositories — five JavaScript, five Python — each with a planted bug, 29 public test assertions and 114 hidden assertions used for grading. Every parameter is published so the run can be criticised or reproduced.
| Parameter | Value |
|---|---|
| Model | MiniMax-M3 via the OpenAI-compatible endpoint |
| Endpoint | POST /v1/chat/completions |
| Temperature / top_p | 0.1 / 0.95 |
| Completion limit | 4,096 tokens |
| Reasoning | Adaptive Thinking explicitly enabled |
| Call budget | Maximum 7 API calls per task |
| Timeout | 180 seconds per request |
| Attempts | One autonomous attempt per task, no retry |
Result: 7 of 10 tasks resolved (70%). Four of five Python tasks passed and three of five JavaScript tasks passed. Across the hidden grader, 92 of 114 assertions passed (80.7%).
The failure pattern is the more useful finding. Every task on which the model actually wrote a candidate file passed the hidden grader — seven out of seven. The three failures never reached a file-write call at all: they exhausted the 4,096-token completion budget while still reasoning. That is not a reasoning-quality failure, it is a budget failure, and it is fixable by raising the completion limit rather than by changing models.
This is exactly the kind of detail a leaderboard number destroys. A published score of 70% reads as “fails three times in ten.” The actual behaviour was “never wrote a wrong fix, but ran out of room to think three times.”
What this test does not tell you
- Ten tasks is a small sample. The 95% confidence interval on 70% runs from 39.7% to 89.2%, which is wide enough that the point estimate should not be quoted on its own.
- These were synthetic micro-repositories, not production codebases with years of accumulated context.
- One harness design, pass@1 only. No other model was run through the identical harness, so this number cannot be compared with any other model’s score.
- It is a point-in-time result from July 28, 2026. Models change.
The complete run, including the per-task breakdown, is published in our coding benchmark write-up. We separately tested tool and function calling reliability in the tool calling benchmark, and put the desktop coding application through five real tasks in our hands-on review, where it passed four of five.
On vendor numbers: MiniMax publishes 59.0% on SWE-Bench Pro and 66.0% on Terminal-Bench 2.1 for the same model. Those are different datasets and different methodologies and should not be compared numerically with our 70%. The same caution applies to every published figure from every vendor, which is the core reason this page does not print a cross-vendor league table.
Test any coding model yourself in thirty minutes
This is the part that actually answers the question for you. Vendor benchmarks run on public datasets that models may have seen during training; your repository is the one dataset that is guaranteed clean. The protocol below is deliberately small enough to finish in one sitting.
- Pick five real bugs you already fixed. Take them from your own commit history, ideally from the last six months so the code still resembles what you work on. You already know the correct answer, which is what makes grading honest.
- Revert each fix on a scratch branch. Keep the failing test that caught it. If there was no test, write the assertion you would have wanted.
- Write one prompt template and reuse it unchanged for every model. Include the failing test output and the relevant files, nothing more. Varying the prompt per model invalidates the comparison.
- Give every model the same budget: the same completion token limit, the same number of attempts, the same timeout. Our own test showed budget, not intelligence, causing every failure — so an unequal budget silently decides the result.
- Grade only on the hidden test passing. Not on whether the code looks good, and not on whether the explanation reads well. Either the assertion passes or it does not.
- Record cost per resolved task, not cost per token. Divide total spend by the number of bugs actually fixed. This number reorders vendor rankings more often than any other.
- Note where each failure occurred: did it write a wrong fix, or did it never write anything? These call for opposite responses — a different model versus a larger budget.
Five tasks will not give you statistical significance, and neither does any public leaderboard at the sample sizes typically used. What it gives you is a directional answer on your own code, which is a better basis for a decision than a number produced on someone else’s.
Choosing by scenario
Where families differ in ways that are stable enough to state without a benchmark:
| If your priority is… | What to weigh most |
|---|---|
| Autonomous multi-file refactors | Tool calling reliability and context window, not raw benchmark score |
| Cost at high volume | Price per resolved task, plus whether open weights let you self-host |
| Working inside an existing IDE | Editor integration quality, which is a tooling question rather than a model question |
| Data staying on your infrastructure | Whether open weights are published and what the licence permits |
| Long files and large repositories | Context window and, more importantly, whether accuracy holds across it |
Product families worth evaluating against your own tasks include ChatGPT, Claude, Gemini, DeepSeek, Qwen and MiniMax. We deliberately do not rank them here, because we have not run them through an identical harness, and publishing a ranking we have not measured would be exactly the failure this page is arguing against.
Where MiniMax fits, honestly
This is a MiniMax reference site, so the bias is worth stating rather than hiding. Based on what we have actually measured:
- It writes correct fixes when it commits to one. Seven of seven file-writes passed the hidden grader in our run. That is a small sample but a clean signal.
- It needs headroom. Every failure we saw was a completion budget exhausted during reasoning. Raise the limit above 4,096 tokens before concluding anything about capability.
- Open weights are available for some models, which matters if your code cannot leave your infrastructure. See running it locally.
- We have not tested it against other models in an identical harness, so we cannot tell you it is better or worse than the alternatives. Run the protocol above on your own repository.
If you want to try it on real work, the practical entry points are MiniMax Code for the desktop application, the CLI for terminal workflows, the VS Code setup for editor integration, and the code generation guide for patterns that work.
Frequently asked questions
Which AI is best for coding in 2026?
No page can answer that for your codebase, including this one. What we can tell you is what we measured: MiniMax-M3 resolved 7 of 10 planted bugs in our July 2026 harness, and every fix it actually wrote passed the hidden grader. Run the thirty-minute protocol above on five of your own bugs to get an answer that applies to your work.
Are vendor benchmark scores reliable?
They are reliable as a statement of what happened in that vendor’s harness. They are not comparable across vendors, because the datasets and methods differ, and public datasets may appear in training data. Treat them as a floor for capability, not a ranking.
Is a bigger context window better for coding?
Only if accuracy holds across it. A large advertised context is a capacity claim, not an accuracy claim. Test with a file at the size you actually work with rather than trusting the headline number.
Should I use an open-weight model for coding?
It is the right choice when code cannot leave your infrastructure, or when volume makes API pricing uneconomic. It costs you the operational burden of hosting and the licence terms need reading before commercial use.
Why does this page not rank the models?
Because we have not run them through an identical harness, and a ranking we have not measured would be an opinion presented as a result. Every number on this page is one we produced and can show the method for.
Test results on this page were produced by MiniMax-AI.chat on the dates stated. Vendor-published figures are attributed to their source and are not comparable with ours.