RankingEffort benchmarks

MiniMax-M2.7 · Reasoning

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32 measured results · 3 effort settings · 32 benchmark/harness combinations

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Capability profile

One configuration, seven capabilities

Comparison score · 0–100

AMiniMax-M2.7 · Reasoning3 of 7 capabilities supported

Gaps are unknown, not zero. Hollow points are preliminary.

Agentic
A14.7Supported
Hard reasoning
A22.1Supported
Coding
A17.4Preliminary
Human pref
Unknown
Knowledge
A23.3Supported
Multimodal
Unknown
Long context
A24.6Preliminary

Each profile belongs to the named configuration. Capability scores use a shared panel for all axes, distinct from the overall ranking panel; they are not accuracy percentages or direct head-to-head wins.

Capability score evidence

MiniMax-M2.7 · Reasoning: 9.1 overall comparison score. Meets the overall evidence requirements.

5 areas measured. The available task mix affects this estimate; missing areas remain unknown.

Shared-result disagreement. This aggregate order conflicts with matched benchmark results against 1 other configurations. Different test coverage and opponents affect the fit. Inspect published results before treating the rank as conclusive.

Which sources carry the weight?

Artificial Analysis · 100.0% direct comparison weight · independent of the model provider

Which benchmark families carry the weight?

IFBench · 7.8% direct comparison weight
Humanity’s Last Exam · 7.7% direct comparison weight
GPQA · 7.6% direct comparison weight
Critpt · 7.6% direct comparison weight
omniscience · 7.6% direct comparison weight
Other families (10) · 61.7% direct comparison weight

These shares describe direct comparison weight, not fractions of the final score or confidence. Source balancing does not give every family equal weight or remove correlations between overlapping sources.

How sensitive is this score?

The audit removes one source at a time and refits against the same overall reference panel. These published results use the matching dataset audited 2026-10-23.

Both ranks use the same surviving configurations for each removal. The cohort starts with 444 configurations supported in the original audit, including separate effort settings. These are diagnostic positions, not the default leaderboard ranks. Eligibility is not rechecked after removal.

What changes when a source is removed?
Removed source	Rank before → after	Rank movement	Cohort remaining / lost support	Score after (change)
Artificial Analysis	Comparison support lost; no rank		203 remaining / 241 lost	Unavailable
ARC Prize	209 → 206	Up 3	444 remaining / 0 lost	11.4 (+2.4 points)
Datacurve	209 → 209	No movement	444 remaining / 0 lost	7.2 (-1.8 points)
LiveCodeBench	209 → 208	Up 1	444 remaining / 0 lost	9.0 (-0.1 points)
Scale	209 → 209	No movement	444 remaining / 0 lost	10.4 (+1.3 points)
Terminal-Bench	209 → 209	No movement	444 remaining / 0 lost	9.0 (-0.1 points)
Cognition	209 → 209	No movement	444 remaining / 0 lost	8.7 (-0.4 points)
LiveBench	208 → 205	Up 3	443 remaining / 1 lost	9.4 (+0.3 points)
SWE-rebench	209 → 209	No movement	444 remaining / 0 lost	9.3 (+0.2 points)
Epoch AI	203 → 202	Up 1	431 remaining / 13 lost	9.3 (+0.2 points)
Vals AI	209 → 209	No movement	443 remaining / 1 lost	8.7 (-0.4 points)
Provider reports · Anthropic	209 → 209	No movement	444 remaining / 0 lost	9.1 (-0.0 points)
Provider reports · Moonshot AI	209 → 209	No movement	444 remaining / 0 lost	8.8 (-0.3 points)
Provider reports · Meta	209 → 209	No movement	444 remaining / 0 lost	9.1 (-0.0 points)
Provider reports · Mistral	209 → 209	No movement	444 remaining / 0 lost	9.1 (+0.0 points)
Provider reports · OpenAI	209 → 209	No movement	444 remaining / 0 lost	9.1 (+0.1 points)
All provider reports	209 → 209	No movement	444 remaining / 0 lost	8.8 (-0.2 points)

Lost support means a configuration no longer has a comparison path to the complete reference panel. Its rank is unavailable. A surviving path alone does not establish leaderboard eligibility.

Overlapping sources may remain correlated even with source balancing. Leave-one-source-out movement is a sensitivity diagnostic, not a spread of source-specific ranks or a confidence measure. Scoring methodology · Dated configuration audit

Every benchmark family

Removed evidence	Recomputed score	Change
arc-agi	11.1	+2.0
deepswe	7.2	-1.8
ifbench	8.1	-1.0
gpqa	8.4	-0.7
mmmu-pro	9.7	+0.6
apex-agents	9.7	+0.6
epoch-game-puzzles	8.6	-0.4
simpleqa	9.5	+0.4
frontiercode	8.7	-0.4
critpt	9.4	+0.4
gdp-pdf	9.4	+0.3
aa-lcr	8.8	-0.3
automationbench	9.4	+0.3
itbench	9.4	+0.3
mmlu-pro	9.3	+0.3
omniscience	8.8	-0.2
swe-rebench	9.3	+0.2
hle	9.3	+0.2
gdpval	8.8	-0.2
livebench-instructions	9.3	+0.2
multi-swe-bench	8.9	-0.2
tau-bench	9.2	+0.2
analyst-agent	9.2	+0.1
livebench-language	9.2	+0.1
terminal-bench-science	9.2	+0.1
arc-agi-3	8.9	-0.1
enigma-eval	9.0	-0.1
harvey	9.0	-0.1
swe-atlas-qna	9.0	-0.1
livebench-coding	9.0	-0.1
briefcase	9.1	+0.1
livebench-reasoning	9.0	-0.1
vibe-code	9.0	-0.1
terminal-bench	9.1	+0.0
aime	9.0	-0.0
swe-atlas-test-writing	9.0	-0.0
math500	9.1	+0.0
vals-code-migration	9.0	-0.0
vals-legal-research	9.0	-0.0
gmmlu	9.1	+0.0
vals-finance-agent	9.0	-0.0
enterprise-ops	9.0	-0.0
vals-tax-agent	9.0	-0.0
livecodebench	9.0	-0.0
vals-excel-modeling	9.1	-0.0
livebench-math	9.1	+0.0
frontiermath	9.1	+0.0
swe-atlas-refactoring	9.1	-0.0
livebench-data	9.1	-0.0
swe-bench-pro	9.1	+0.0

What evidence is missing from the fit?

Across all collected settings for this model: 90 observations, 19 contributing, 0 matched without graph weight and 71 excluded. Counts do not establish rank eligibility.

1 · Benchmark family or source protocol has not been reviewed
2 · Original SciCode grading defects are under review; retained as supporting evidence, excluded from aggregate and capability scores. Corrected SciCode-Verified requires a separate protocol review. Sources: https://arxiv.org/abs/2608.04975 and https://artificialanalysis.ai/evaluations/scicode
10 · Effort is not identified
1 · Evaluator has not been reviewed for independent admission
22 · No reviewed effort for this catalog observation; any separately collected effort measurement is counted separately
34 · Supporting evidence outside the reviewed capability core
1 · Narrow specialist biological, medical or security evaluation; preserved as raw evidence outside general capability scoring.

Inspect every observation and exclusion →

Benchmark results

These are published benchmark scores. Supported effort estimates also appear on the Capability leaderboard. Results from different harnesses occupy different rows. Missing cells stay unknown; no scores are copied between effort levels.

Benchmark / source	High	Reasoning	Unspecified
AA-AnalystAgentArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of tasks solved on all 5 of 5 attempts · 80 tasks, 5 attempts per task · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	11.25% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
AA-Briefcase EloArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better · Evaluation results measured independently by Artificial Analysis Unit: elo. Source collected 2026-10-18.	Not reported	718.55 Elo Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
APEX-Agents-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	10.62% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
AA-LCR v1.1Artificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	78.33% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
AutomationBench-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of task objectives completed with no guardrail violations · Higher is better · Benchmark developed by Zapier · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	4.44% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
CritPtArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by Argonne and UIUC, with contributions from 60+ researchers globally. CritPt is under review by its authors following external feedback · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	0.57% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
GDP.pdf: All-pass RateArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of attempts where every atomic criterion passed · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	6% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
GDPval-AA v2.1 LeaderboardArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Elo rating for performance on real-world work tasks · Anchored to DeepSeek V4.1 Flash (max) at 1600 · Higher is better · Evaluation results measured independently by Artificial Analysis Unit: elo. Source collected 2026-10-18.	Not reported	1,010.1 Elo Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
GPQA DiamondArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	87.37% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
Humanity's Last ExamArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	29.61% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
IFBenchArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	75.71% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
ITBench-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Combined public and private Site Reliability Engineering (SRE) tasks · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	26.46% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
MLCR-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of tasks judged accurate, complete, and concise · Expert + compound (hard) sets · Benchmark developed by Wisedocs · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	3.33% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
AA-Omniscience IndexArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis Unit: index. Source collected 2026-10-18.	Not reported	0.77 Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
AA-Omniscience AccuracyArtificial Analysis · 6000 questions; 1 attempt; correct-answer share; GPT-5.6 Luna medium grader Benchmark details AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	26.8% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
SciCodeArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark curated by scientists across 16 disciplines. SciCode is under review following an independent audit of the dataset · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	50.12% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
𝜏²-Bench TelecomArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	84.8% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
𝜏³-BankingArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by Sierra Research · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	9.9% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
Terminal-Bench HardArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	39.39% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
Terminal-Bench v2.1Artificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by the Laude Institute and open-source contributors · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	55.43% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
Terminal-Bench v4.0Artificial Analysis · mini-SWE-agent v2.4.6; 66 tasks; pass@1 averaged over 3 repeats; 500 steps; task timeout up to 8 hours Benchmark details Benchmark developed by the Laude Institute, Stanford, and open-source contributors · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	0% Reported settings MiniMax-M2.7; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/minimax-m2-7; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
Vals AI · Finance Agent v2Vals AI · Vals FAB v2 shared six-tool harness; two-hour task limit Benchmark details Overall weighted rubric accuracy, averaged over three runs on 450 tasks. Dealbreaker failures and timeouts score zero. Related categories and all-pass scores are not additional votes. Three-model judge panel; private test set. Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	27.89% Reported settings {"checkpoint":"MiniMax-M2.7","evaluatorModel":"minimax/MiniMax-M2.7","sourceUpdatedOn":"2026-10-16","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":1,"top_p":0.95,"max_output_tokens":196608,"verbosity":null,"provider":"MiniMax","harness":null}
Vals AI · Code Migration v1Vals AI · Vals Code Migration shared agent harness Benchmark details Hidden behavior-test pass rate, equally weighted across 40 repositories (30 CLI and 10 COBOL-to-Java). Anti-cheat failures score zero. Code-quality ratings and language splits are not additional votes. Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	8.69% Reported settings {"checkpoint":"MiniMax-M2.7","evaluatorModel":"minimax/MiniMax-M2.7","sourceUpdatedOn":"2026-10-16","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":1,"top_p":0.95,"max_output_tokens":80000,"verbosity":null,"provider":"MiniMax","harness":null}
Vals AI · Legal Research Bench v1Vals AI · Vals Legal Research shared five-tool harness; three-hour task limit Benchmark details All-pass accuracy on 208 private legal research tasks. Every expert rubric check must pass; GPT-5.4 judges responses. Partial-credit scores and practice-area splits are not additional votes. Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	10.58% Reported settings {"checkpoint":"MiniMax-M2.7","evaluatorModel":"minimax/MiniMax-M2.7","sourceUpdatedOn":"2026-10-16","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":1,"top_p":0.95,"max_output_tokens":80000,"verbosity":null,"provider":"MiniMax","harness":null}
Vals AI · Excel Modeling Benchmark v1Vals AI · Vals EMB shared workbook agent harness; 3.5-hour task limit Benchmark details Mean of seven equally weighted task categories. Template submissions use recalculated Excel cell accuracy; scratch submissions use rubric checks judged by GPT-5.4 Mini XHigh. Partial credit does not mean task completion. Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	28.45% Reported settings {"checkpoint":"MiniMax-M2.7","evaluatorModel":"minimax/MiniMax-M2.7","sourceUpdatedOn":"2026-10-16","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":1,"top_p":0.95,"max_output_tokens":80000,"verbosity":null,"provider":"MiniMax","harness":null}

What these comparisons mean

Each source reports its own effort labels. Matching labels do not establish equal compute budgets or identical fallback behavior. Open Reported settings to inspect the exact model name, agent and task coverage. Multiple results at one level remain visible rather than selecting the best. Source-reported None or Non-reasoning labels are not treated as evidence that an API supports those settings.

These measurements use their own dated source collection. The leaderboard fits identified configurations jointly; it contains no pooled model ratings. Explicit thinking budgets are separate configurations. Read the methodology.

Download all effort benchmark results · 8,916 results across 259 models · Collected 2026-10-18

MiniMax-M2.7 · Reasoning

Compare with

One configuration, seven capabilities

Agentic

Hard reasoning

Coding

Human pref

Knowledge

Multimodal

Long context

Capability score evidence

Benchmark results