MiniMax M2.7
MiniMax · Open Weight · rank 88 · bench-align-v5
Canonical idminimax-m2-7
Access typeOpen Weight
Blended $/1M$0.5275% input / 25% output
Capability shape
Seven axes from the ranking source. A missing axis is drawn as a gap.
Runtime service evidence
Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim.
Time to first token: latency to the first answer chunk (Artificial Analysis, via BenchLM). Reasoning models include thinking time, so values can run to tens or hundreds of seconds.
Evidence key: Observed
| Measurement | Value | Observed | Last good | Evidence |
|---|---|---|---|---|
| Time to first token | 47.9 s | 2026-09-17 | 2026-09-17 | |
| Throughput | 53 tok/s | 2026-09-17 | 2026-09-17 |
Regional or per-endpoint measurements appear only when the API supplies them; none are modelled here.
Endpoint and price matrix
Every published price component, including cache reads and writes.
| Component | USD | Evidence |
|---|---|---|
| Input / 1M tokens | $0.30 | |
| Output / 1M tokens | $1.20 | |
| Cache read / 1M tokens | Unavailable | |
| Cache write / 1M tokens | Unavailable | |
| Blended / 1M (75% input / 25% output) | $0.52 |
Self-hosted listingOfficial MiniMax pay-as-you-go API rate for the open-weight MiniMax-M2.7 release ($0.3/$1.2 per 1M tokens; prompt caching read $0.06, write $0.375).The rates above are a hosted price matched from another provider, not a first-party list price.
Workload-aware monthly cost example
10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. This uses the same calculator as the cost simulator, so an unavailable applicable rate makes the example unavailable too.
Benchmark record
40 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| GPQA-D GPQA Diamond | 87.0 | Graduate-level science questions | Graduate level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index | 23.2 | Cross-benchmark intelligence index | Display-only external reference | Artificial Analysis |
| AA-GPQA Diamond Artificial Analysis GPQA Diamond | 87.4 | Graduate-level science questions | Graduate-level science reasoning | Artificial Analysis GPQA Diamond Benchmark Leaderboard |
| AA-HLE Artificial Analysis Humanity's Last Exam | 29.6 | Expert-level questions | Frontier expert reasoning | Artificial Analysis Humanity's Last Exam Benchmark Leaderboard |
| AA-Omniscience Index Artificial Analysis Omniscience Index | 0.8 | Knowledge questions | Broad factual knowledge | AA-Omniscience: Knowledge and Hallucination Benchmark |
| AA-Omniscience Accuracy Artificial Analysis Omniscience Accuracy | 26.8 | Knowledge questions | Broad knowledge | Artificial Analysis model benchmarks |
| AA-Omniscience Hallucination Rate Artificial Analysis Omniscience Hallucination Rate | 35.6 | Knowledge questions | Factuality | Artificial Analysis model benchmarks |
| MMLU-Pro (Arcee) MMLU-Pro first-party comparison snapshot | 80.8 | Professional academic QA | Professional level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| GPQA Diamond (Vals) GPQA Diamond, Vals AI run | 86.6 | Graduate-level science questions | Expert reasoning | Vals AI GPQA Diamond, Vals AI run leaderboard |
| MMLU-Pro (Vals) MMLU-Pro, Vals AI run | 80.4 | Academic multiple-choice questions | Broad academic knowledge | Vals AI MMLU-Pro, Vals AI run leaderboard |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| SWE-Rebench SWE-Rebench | 51.9 | Fresh GitHub issues (rolling window) | Professional software engineering | SWE-Rebench: Contamination-Free Evaluation of Software Engineering Agents |
| SWE-bench Pro SWE-bench Pro | 56.2 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
| SWE Multilingual SWE Multilingual | 76.5 | Multilingual software-engineering tasks | Professional software engineering | MiniMax M2.7: Early Echoes of Self-Evolution |
| Multi-SWE Bench Multi-SWE Bench | 52.7 | Multi-language repo tasks | Professional software engineering | MiniMax M2.7: Early Echoes of Self-Evolution |
| VIBE-Pro VIBE-Pro | 55.6 | Full project delivery tasks | End-to-end software delivery | MiniMax M2.7: Early Echoes of Self-Evolution |
| Vibe Code Bench Vibe Code Bench v1.1 | 27.04 | End-to-end web application builds | End-to-end software delivery | Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development |
| NL2Repo NL2Repo | 39.8 | Natural language to repository tasks | System-level software comprehension | MiniMax M2.7: Early Echoes of Self-Evolution |
| React Native Evals React Native Evals | 71.4 | React Native app implementation tasks | Production mobile app engineering | React Native Evals |
| SWE-bench Verified* SWE-bench Verified (mini-swe-agent-v2) | 75.4 | Repository task completion | Professional software engineering | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
| AA Coding Index Artificial Analysis Coding Index | 52.6 | Cross-benchmark coding index | Display-only external reference | Artificial Analysis model leaderboards |
| AA-SciCode Artificial Analysis SciCode | 50.1 | Scientific coding subproblems | Scientific programming | Artificial Analysis SciCode Benchmark Leaderboard |
| LiveCodeBench (Vals) LiveCodeBench, Vals AI run | 79.9 | Competitive programming problems (easy, medium, hard) | Frontier coding | Vals AI LiveCodeBench, Vals AI run leaderboard |
| SWE-bench (Vals) SWE-bench, Vals AI run | 73.8 | Real repository issues by human time bucket | Frontier coding agents | Vals AI SWE-bench, Vals AI run leaderboard |
Mathematics
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| AIME25 (Arcee) AIME25 first-party comparison snapshot | 80.0 | 15 problems | High school olympiad level | Trinity-Large-Thinking: Scaling an Open Source Frontier Agent |
Reasoning
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| AA-LCR Artificial Analysis Long Context Reasoning | 78.3 | Long-context reasoning tasks | Long-context reasoning | Artificial Analysis model benchmarks |
| CritPt Critical Physics Tasks | 0.6 | Research-level physics questions | Research-level physics reasoning | CritPt Benchmark Leaderboard |
Instruction Following
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| AA-IFBench Artificial Analysis IFBench | 75.7 | Verifiable instruction constraints | Instruction precision | Artificial Analysis IFBench Benchmark Leaderboard |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.0 Terminal-Bench 2.0 | 57 | Terminal-based software tasks | Professional software engineering | Terminal-Bench 2.0 |
| GDPval-AA GDPval-AA | 1087 | Agentic real-world work tasks | Professional agentic workflows | DeepSeek-V4 Technical Report |
| GDPval-AA GDPval-AA normalized | 29.4 | Economically valuable tasks | Professional agentic workflows | Artificial Analysis model benchmarks |
| AA Agentic Index Artificial Analysis Agentic Index | 16.8 | Cross-benchmark agentic index | Display-only external reference | Artificial Analysis model leaderboards |
| APEX-Agents-AA APEX-Agents-AA | 10.6 | 452 professional-services agent tasks | Long-horizon workplace agent tasks | APEX-Agents-AA Benchmark Leaderboard |
| Gert Labs Gert Labs Composite Game Benchmark | 40.40 | Novel game environments | Agentic coding and decision-making | Gert Labs rankings |
| Toolathlon Toolathlon | 46.3 | Multi-tool workflows | Advanced tool use | Introducing GPT-5.4 mini and nano |
| τ²-bench results τ²-Bench Tool-Agent-User Evaluation | 84.8 | Airline, retail, and telecom customer-service task sets | Dual-control customer-service workflows | τ²-Bench: Evaluating Conversational Agents in a Dual-Control Environment |
| MLE-Bench Lite MLE-Bench Lite | 66.6 | Low-resource ML competitions | Agentic machine learning | MiniMax M2.7: Early Echoes of Self-Evolution |
| MM-ClawBench MM-ClawBench | 62.7 | OpenClaw-style real-world tasks | Broad real-world agentic execution | MiniMax M2.7: Early Echoes of Self-Evolution |
| Claw-Eval Claw-Eval | 48.7 | 300 tasks, 2,159 rubrics | Real-world general, multi-turn, and native multimodal agent execution | Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents |
| Terminal-Bench 2.1 (Vals) Terminal-Bench 2.1, Vals AI run | 48.7 | Difficult terminal tasks | Frontier agentic | Vals AI Terminal-Bench 2.1, Vals AI run leaderboard |
Multimodal & Grounded
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Design Arena Website Design Arena Website Elo | 1257 | Website generation comparisons | Design and website generation | OpenRouter Grok 4.3 benchmarks |
Lifecycle and limitations log
Lifecycle events the source associates with this model, plus what this profile does not claim.
No lifecycle event references this model.
That is not evidence the model has no lifecycle plan — only that this source published none.
What this profile does not claimValues are reproduced exactly as their sources published them, in the units those sources declared; none are converted, interpolated, or averaged across providers. Any field marked unavailable was attempted and not returned — the attempt timestamp is in each badge. Last attempted fetch for this model’s score: 2026-09-17 17:17 UTC.