MiniMax M3: Official MiniMax M3 release: coding benchmarks, long context, and real long-task cases

Test environment

• Model: MiniMax M3, a MoE model with approximately 428B total parameters and approximately 23B active parameters; MiniMax Sparse Attention (MSA) supports up to a 1M context; native image/video input and computer use.

• Public benchmarks: SWE-Bench Pro, Terminal-Bench 2.1, SWE-fficiency, KernelBench Hard, MCP Atlas, and PostTrainBench.

• Long-task cases: reproducing a paper, optimizing NVIDIA Hopper FP8 GEMM, and having the model autonomously post-train four foundational models.

• Evaluation method: the official internal infrastructure, with some tasks using harnesses such as Claude Code/Terminus 2; the official description says SWE-Bench Verified was run four times and averaged, while other metrics used the corresponding sandbox and timeout settings.

Inputs/configuration

• The official benchmarks used M3's coding/Agent configuration; Terminal-Bench 2.1 used an 8C16G sandbox, a two-hour timeout, a 128K maximum output, and Terminus 2 scaffolding.

• Paper reproduction: the paper, code, and experiment logs were placed in the long-task context, and the model ran autonomously for nearly 12 hours.

• CUDA optimization: the task description, benchmark script, and a Triton skeleton that could not be run directly were provided, without a reference high-performance implementation; the model iterated using benchmark feedback.

Result data

• Paper reproduction: nearly 12 hours, 18 commits, and 23 experiment figures; the core experiments were completed and the main trends reproduced.

• CUDA FP8 GEMM: approximately 24 hours, 147 benchmark submissions, and 1,959 tool calls; peak utilization rose from 7.6% to 71.3%, or 9.4× relative to the initial version.

• The official source says that under a 1M context, MSA requires approximately 1/20 of the per-token computation of the previous generation, with more than 9× prefill acceleration and more than 15× decode acceleration; these are architecture/service-side figures, not a general end-user throughput commitment.

Conclusion

The evidence for M3 is concentrated in long-horizon coding, iterative tool feedback, and multimodal Agents, rather than simple single-turn chat. The official cases show that it can continue exploring for an extended period under clear benchmark feedback; its benchmark scores are in a usable frontier range, but they cannot be interpreted independently of the harness and internal evaluation methodology.

Limitations

• All results are vendor-reported; some comparison scores come from official leaderboards or different harnesses. The official source did not disclose all raw trajectories, failure samples, or random seeds.

• Paper reproduction and CUDA optimization are selected cases and cannot represent the success rate on ordinary repositories.

• The 1M context and MSA acceleration figures do not mean that every API provider offers the same context, latency, or pricing.

Reproduction steps

1. For public coding tasks, fix the sandbox, tools, timeout, output limit, and harness, and run at least three times.

2. Reproduce experimental long tasks separately: record every submission, benchmark score, tool call, token count, wall-clock time, and human intervention.

3. Report model capability, harness orchestration, and hardware/service throughput separately; do not substitute selected cases for task-set statistics.

4. For tasks requiring image/video input, verify that the actual API endpoint provides native multimodality and the same billing terms.

Original evidence and data

• The official release page clearly lists the five coding/Agent benchmarks above and the PostTrainBench comparison values.

• The official release page discloses the timing, submission counts, tool-call counts, and result changes for the 12-hour paper reproduction and 24-hour CUDA optimization.

• The official evaluation-method description explains the main harness/sandbox settings for SWE-Bench Verified, Terminal-Bench 2.1, and NL2Repo.

Source excerpt or observation (compliance short quote only)

• The official positioning puts “frontier coding, 1M context, native multimodality” in the same model; actual selection still needs to return to the specific toolchain and feedback loop.