Capability
55.1/100
field median 56.2
#88 of 232 ranked models
Superseded.MiniMax has newer models in this line:MiniMax M3
MiniMax M2.7
SupersededReleased Mar 18, 2026Open WeightNon-Reasoning200K context
Released Mar 18, 2026 — see all recent releases
Decision reading
MiniMax M2.7 scores 55.1 out of 100 and ranks #88 of 232. This profile shows 23 source-displayable benchmark rows; its strongest eligible category is Coding at #68. API pricing is $0.3 input and $1.2 output per million tokens.
Data as of September 15, 2026 · How the score is built
Share or export
Strongest published evidence
Coding ranks #68. Particularly well-suited for software development and code generation tasks.
Validate before choosing
23 published rows leave some tracked benchmark slots empty. Agentic is its lowest eligible category at #110.
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Price
$0.30input / $1.20 output
input median $0.95
blended $0.75
Speed
53tok/s
field median 91 tok/s
First token 47.9 s
Context
200Ktokens
field median 256,000
Reported for this model; direct source link not stored
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
MiniMax M2.7 category percentile values
- Agentic28th percentile
- Coding56th percentile
- ReasoningNot eligible
- Knowledge51st percentile
- MathNot eligible
- MultilingualNot eligible
- MultimodalNot eligible
- Instruction followingNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#110/153
- Coding#68/152
- ReasoningNot ranked
- Knowledge#90/183
- MathNot ranked
- MultilingualNot ranked
- MultimodalNot ranked
- Inst. FollowingNot ranked
Top decileTop quartileMid-fieldNot eligible
What it costs to get this score
Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Current modelMiniMax M2.7 · 55.1 score · $0.75 blended per million tokens
Horizontal: blended price per million tokens, log scale · Vertical: public score
How much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic7/7 verified
- Coding10/11 verified
- ReasoningNot measured
- Knowledge2/4 verified
- Math0/1 verified
- MultilingualNot measured
- MultimodalNot measured
- Inst. FollowingNot measured
Verified sourceProvisionalNot measured
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 200K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Superseded
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #110 of 153Percentile 28thWeight 22%7 benchmarksVerified | 41.1 | #110 of 153 | 28th | 22% | 7 benchmarks | Verified |
| CodingRank #68 of 152Percentile 56thWeight 20%11 benchmarksMixed sources | 48.6 | #68 of 152 | 56th | 20% | 11 benchmarks | Mixed sources |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeRank #90 of 183Percentile 51stWeight 12%4 benchmarksMixed sources | 48.7 | #90 of 183 | 51st | 12% | 4 benchmarks | Mixed sources |
| MathRank Not rankedWeight 5%1 benchmarkReported | 81.3 | Not ranked | Not available | 5% | 1 benchmark | Reported |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding11 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score56.2% | Versus best verified row Best verified: Claude Fable 5.1 · 81.2% | Gap25 behind | WeightWeighted 25% | Provider exact |
| LiveCodeBench (Vals)LiveCodeBench, Vals AI run | Score79.9% | Versus best verified row Best verified: Claude Fable 5.1 · 90.5% | Gap10.6 behind | WeightWeighted 15% | |
| SWE-Rebench | Score51.9% | Versus best verified row Best verified: Claude Opus 4.6 · 65.3% | Gap13.4 behind | WeightWeighted 10% | Benchmark exact |
| SWE Multilingual | Score76.5% | Versus best verified row Best verified: Claude Opus 5 · 89.5% | Gap13 behind | WeightWeighted 5% | Provider exact |
| SWE-bench Verified*SWE-bench Verified (mini-swe-agent-v2) | Score75.4% | Versus best verified row | GapNo verified comparator | WeightDisplay only | Secondary exact |
| Multi-SWE Bench | Score52.7% | Versus best verified row Best verified: MiniMax M2.7 · 52.7% | GapBest verified | WeightDisplay only | Provider exact |
| VIBE-Pro | Score55.6% | Versus best verified row Best verified: MiniMax M2.7 · 55.6% | GapBest verified | WeightDisplay only | Provider exact |
| NL2Repo | Score39.8% | Versus best verified row Best verified: DeepSeek V4.1 Flash · 65.4% | Gap25.6 behind | WeightDisplay only | Provider exact |
| Vibe Code BenchVibe Code Bench v1.1 | Score27.04% | Versus best verified row Best verified: Claude Opus 4.7 · 71.00% | Gap44 behind | WeightDisplay only | Benchmark exact |
| React Native Evals | Score71.4% | Versus best verified row Best verified: Composer 2 · 96.1% | Gap24.7 behind | WeightDisplay only | Benchmark exact |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score73.8% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap23.2 behind | WeightDisplay only | Verified |
Agentic7 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.0 | Score57% | Versus best verified row Best verified: GPT-5.6 Sol · 91.9% | Gap34.9 behind | WeightWeighted 30% | Provider exact |
| Toolathlon | Score46.3% | Versus best verified row Best verified: Muse Spark 1.1 · 75.6% | Gap29.3 behind | WeightDisplay only | Provider exact |
| MLE-Bench Lite | Score66.6% | Versus best verified row Best verified: Atria Dawn Preview · 86.2% | Gap19.6 behind | WeightDisplay only | Provider exact |
| MM-ClawBench | Score62.7% | Versus best verified row Best verified: MiniMax M2.7 · 62.7% | GapBest verified | WeightDisplay only | Provider exact |
| Claw-Eval | Score48.7% | Versus best verified row Best verified: Ornith-1.5-397B · 81.4% | Gap32.7 behind | WeightDisplay only | Benchmark exact |
| Gert LabsGert Labs Composite Game Benchmark | Score40.40% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap32.6 behind | WeightDisplay only | Benchmark exact |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | Score48.7% | Versus best verified row Best verified: GPT-6 Astra · 87.3% | Gap38.6 behind | WeightDisplay only |
Knowledge4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | Score80.4% | Versus best verified row Best verified: Claude Fable 5.1 · 92.4% | Gap12 behind | WeightWeighted 10% | Verified |
| GPQA-DGPQA Diamond | Score87.0% | Versus best verified row Best verified: GPT-6 Astra · 96.0% | Gap9 behind | WeightDisplay only | Secondary exact |
| MMLU-Pro (Arcee)MMLU-Pro first-party comparison snapshot | Score80.8% | Versus best verified row Best verified: Trinity-Large-Preview · 75.2% | Gap5.6 behind | WeightDisplay only | Secondary exact |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | Score86.6% | Versus best verified row Best verified: Gemini 3.1 Pro · 95.5% | Gap8.9 behind | WeightDisplay only |
Math1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME25 (Arcee)AIME25 first-party comparison snapshot | Score80.0% | Versus best verified row Best verified: Trinity-Large-Preview · 24.0% | Gap56 behind | WeightDisplay only | Secondary exact |
Lineage
The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Oct 1, 2025
MiniMax M2.5Score 54.9 · $0.3 / $1.2
Mar 18, 2026 · you are here
MiniMax M2.7Score 55.1 · $0.3 / $1.2
Jun 1, 2026
MiniMax M3Score 61.5 · $0.3 / $1.2
Base entry
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
MiniMax M2.7 ranks #88 of 232 on the public leaderboard with a score of 55.14/100. Its source-verified position is #59 of 130.
MiniMax M2.7 is a open weight model with a 200K context window. No explicit reasoning mode is documented in this profile.
Official exact-value snapshot based on MiniMax's live Hugging Face model card and March 18, 2026 launch report. BenchLM only carries the exact benchmarks MiniMax publishes there directly.
Its explicit predecessor is MiniMax M2.5. 23 of 439 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #68, while its lowest eligible position is Agentic at #110. particularly well-suited for software development and code generation tasks.
Frequently asked questions
How does MiniMax M2.7 perform overall in AI benchmarks?
MiniMax M2.7 ranks #88 out of 232 models on the public BenchAlign leaderboard, with a score of 55.14/100. Its evidence status is Supported, and this profile shows 23 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is MiniMax M2.7 good for knowledge and understanding?
MiniMax M2.7 ranks #90 out of 183 eligible models for knowledge and understanding, with a public category score of 48.7/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is MiniMax M2.7 good for coding and programming?
MiniMax M2.7 ranks #68 out of 152 eligible models for coding and programming, with a public category score of 48.6/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is MiniMax M2.7 good for mathematics?
MiniMax M2.7 has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is MiniMax M2.7 good for agentic tool use and computer tasks?
MiniMax M2.7 ranks #110 out of 153 eligible models for agentic tool use and computer tasks, with a public category score of 41.1/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is MiniMax M2.7 open source?
MiniMax M2.7 is an open-weight model from MiniMax. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does MiniMax M2.7 have full benchmark coverage on BenchLM?
No. MiniMax M2.7 currently has 40 source-displayable rows across 439 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of MiniMax M2.7?
MiniMax M2.7 has a reported context window of 200K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.
Compare MiniMax M2.7 with every tracked model485 comparisons
Last updated September 15, 2026. Runtime fields remain blank until a sourced snapshot exists.
Watch MiniMax M2.7 in the weekly brief
Get one weekly email when material rank, price, availability, or benchmark evidence changes are worth revisiting.
Read a sample issueJoin 2,000+ readers.