RankingEffort benchmarks

Mistral Large 3 · Non-reasoning

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25 measured results · 2 effort settings · 25 benchmark/harness combinations

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Capability profile

One configuration, seven capabilities

Comparison score · 0–100

AMistral Large 3 · Non-reasoning4 of 7 capabilities supported

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

Agentic
A5.5Supported
Hard reasoning
A2.9Supported
Coding
A3.7Supported
Human pref
Unknown
Knowledge
A6.0Supported
Multimodal
A3.5Preliminary
Long context
A5.8Preliminary

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

Mistral Large 3 · Non-reasoning: 1.2 overall comparison score. Meets the overall evidence requirements.

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

Which sources carry the weight?

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

Which benchmark families carry the weight?

Aime · 6.3% direct comparison weight
MMLU-Pro · 6.3% direct comparison weight
LiveCodeBench · 6.3% direct comparison weight
IFBench · 6.1% direct comparison weight
Humanity’s Last Exam · 6.0% direct comparison weight
Other families (12) · 69.0% 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 2026-10-20.

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	339 → 338	Up 1	444 remaining / 0 lost	1.6 (+0.4 points)
Datacurve	339 → 339	No movement	444 remaining / 0 lost	0.9 (-0.3 points)
LiveCodeBench	339 → 339	No movement	444 remaining / 0 lost	1.2 (-0.0 points)
Scale	339 → 339	No movement	444 remaining / 0 lost	1.4 (+0.2 points)
Terminal-Bench	339 → 339	No movement	444 remaining / 0 lost	1.2 (-0.0 points)
Cognition	339 → 339	No movement	444 remaining / 0 lost	1.2 (-0.0 points)
LiveBench	338 → 338	No movement	443 remaining / 1 lost	1.3 (+0.1 points)
SWE-rebench	339 → 338	Up 1	444 remaining / 0 lost	1.2 (+0.0 points)
Epoch AI	326 → 323	Up 3	431 remaining / 13 lost	1.2 (+0.0 points)
Vals AI	338 → 338	No movement	443 remaining / 1 lost	1.2 (-0.0 points)
Provider reports · Anthropic	339 → 339	No movement	444 remaining / 0 lost	1.2 (-0.0 points)
Provider reports · Moonshot AI	339 → 339	No movement	444 remaining / 0 lost	1.2 (-0.0 points)
Provider reports · Meta	339 → 339	No movement	444 remaining / 0 lost	1.2 (-0.0 points)
Provider reports · Mistral	339 → 339	No movement	444 remaining / 0 lost	1.2 (+0.0 points)
Provider reports · OpenAI	339 → 339	No movement	444 remaining / 0 lost	1.2 (+0.0 points)
All provider reports	339 → 339	No movement	444 remaining / 0 lost	1.2 (-0.0 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	1.5	+0.3
hle	1.5	+0.3
deepswe	0.9	-0.3
omniscience	1.0	-0.3
mmmu-pro	1.3	+0.1
epoch-game-puzzles	1.1	-0.1
simpleqa	1.3	+0.1
aime	1.3	+0.1
livecodebench	1.3	+0.0
automationbench	1.2	-0.0
swe-rebench	1.2	+0.0
frontiercode	1.2	-0.0
gdpval	1.2	-0.0
livebench-instructions	1.2	+0.0
terminal-bench	1.2	-0.0
briefcase	1.2	-0.0
gdp-pdf	1.2	+0.0
multi-swe-bench	1.2	-0.0
gmmlu	1.2	-0.0
apex-agents	1.2	+0.0
arc-agi-3	1.2	-0.0
livebench-language	1.2	+0.0
aa-lcr	1.2	-0.0
terminal-bench-science	1.2	+0.0
gpqa	1.2	-0.0
enigma-eval	1.2	-0.0
livebench-coding	1.2	-0.0
swe-atlas-qna	1.2	-0.0
harvey	1.2	-0.0
analyst-agent	1.2	+0.0
itbench	1.2	-0.0
math500	1.2	+0.0
livebench-reasoning	1.2	-0.0
vibe-code	1.2	-0.0
tau-bench	1.2	-0.0
ifbench	1.2	+0.0
swe-atlas-test-writing	1.2	-0.0
vals-code-migration	1.2	-0.0
vals-finance-agent	1.2	-0.0
vals-legal-research	1.2	-0.0
vals-tax-agent	1.2	-0.0
critpt	1.2	-0.0
mmlu-pro	1.2	-0.0
vals-excel-modeling	1.2	-0.0
frontiermath	1.2	+0.0
livebench-math	1.2	+0.0
enterprise-ops	1.2	+0.0
swe-atlas-refactoring	1.2	-0.0
swe-bench-pro	1.2	+0.0
livebench-data	1.2	-0.0

What evidence is missing from the fit?

Across all collected settings for this model: 32 observations, 20 contributing, 0 matched without graph weight and 12 excluded. Counts do not establish rank eligibility.

1 · Benchmark family or source protocol has not been reviewed
1 · 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
3 · Effort is not identified
4 · Supporting evidence outside the reviewed capability core
2 · No reviewed effort for this catalog observation; any separately collected effort measurement is counted separately
1 · No matched opponent in this evaluation unit

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	Non-reasoning	Unspecified
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.	228.3 Elo Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
AIME 2025Artificial 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.	38% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	36% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	1.5% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	0% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	1.2% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	420.4 Elo Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
Global-MMLU-LiteArtificial 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.	80.33% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	67.98% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	4.17% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	36.19% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
LiveCodeBenchArtificial 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.	46.46% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
MMLU-ProArtificial 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.	80.65% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
MMMU-ProArtificial 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.	55.66% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	-39.57 Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	24.95% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	36.57% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	24.56% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	5.77% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	15.91% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	11.99% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; 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.	0% Reported settings Mistral Large 3; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/mistral-large-3; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
DAYJOB HealthcareSurge AI · OpenHands SDK 1.43.1 inside Harbor 0.22.0. Judge is Claude Opus 4.8. Score is the board's strict pass@1 percent. Benchmark details DAYJOB Healthcare strict pass@1 percent on the public Surge board. tau = 1, five attempts per task, 50 tasks in the paper and the Hugging Face dataset card. The page meta description says 65 assignments. That count is not on the scoreboard and is not used as n. An attempt passes only when every binary rubric criterion is met. Finance is a separate benchmark. Display only. Not an average and not GDPval-AA. Unit: percent. Source collected 2026-10-18.	Not reported	0% Reported settings Mistral Large 3; effort unspecified. Strict pass@1 percent, tau = 1, five attempts, 50 DAYJOB: Healthcare tasks. An attempt passes only when every binary rubric criterion is met. OpenHands SDK 1.43.1 in Harbor 0.22.0. Judge: Claude Opus 4.8.
DAYJOB FinanceSurge AI · OpenHands SDK 1.43.1 inside Harbor 0.22.0. Judge is Claude Opus 4.8. Score is the board's strict pass@1 percent. Benchmark details DAYJOB Finance strict pass@1 percent on the public Surge board. tau = 1, five attempts per task, 80 tasks in the paper and the Hugging Face dataset card. An attempt passes only when every binary rubric criterion is met. Healthcare is a separate benchmark. Display only. Not an average and not GDPval-AA. Unit: percent. Source collected 2026-10-18.	Not reported	0% Reported settings Mistral Large 3; effort unspecified. Strict pass@1 percent, tau = 1, five attempts, 80 DAYJOB: Finance tasks. An attempt passes only when every binary rubric criterion is met. OpenHands SDK 1.43.1 in Harbor 0.22.0. Judge: Claude Opus 4.8.
ThinkingBox-BenchMicrosoft and Hugging Face · ThinkingBox sandbox. Shared prompts, MCP tools, simulated user, and executable checks over terminal backend state. Benchmark details Task-weighted pass@1 on 507 stateful business workflows, 20 trials each. The stored score is the published overall from the 3 October 2026 blog. Display only. Excluded from weighted Overall. Unit: percent. Source collected 2026-10-18.	Not reported	4.66% Reported settings Mistral-Large-3; ThinkingBox sandbox; shared prompts, tools, simulated user, and executable terminal-state checks; pass@1; 20 trials on each of 507 tasks; published task-weighted overall 4.66. Retail 11.28 (98); Auto insurance 1.30 (100); Travel 8.99 (104); Neobank 1.15 (104); Consulting 0.74 (101). Blog post 2026-10-03. The post does not name a reasoning-effort dial. Display only. Excluded from weighted Overall.

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 · 8,915 results across 259 models · Collected 2026-10-18

Mistral Large 3 · Non-reasoning

Compare with

One configuration, seven capabilities

Agentic

Hard reasoning

Coding

Human pref

Knowledge

Multimodal

Long context

Capability score evidence

Benchmark results