Model comparison

GPT-5.5 vs Mistral Large

GPT-5.5 is the stronger model overall, scoring 63.4 to 31.9 on the Noometry Index. Mistral Large costs 3.8× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

Last verified October 10, 2026. 30 shared benchmarks.

GPT-5.5 OpenAI

63.4

Rank #9 Confirmed

Mistral Large

Mistral AI

31.9

Rank #263 Confirmed

Summary

They share 30 benchmarks with published results for both. GPT-5.5 scores higher in 9 categories and Mistral Large in 0 categories; 9 gaps are clear of the uncertainty.
The widest gap is in math, where GPT-5.5 leads 81.7 to 18.2.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.5 and 8.5% for Mistral Large.
Mistral Large is cheaper at $2 / $6 per million input/output tokens, against $5 / $30 for GPT-5.5.
GPT-5.5 accepts more context: 1.05M tokens versus 131K.
Mistral Large has downloadable open weights; the other is API-only.

Side by side

GPT-5.5 and Mistral Large specifications
	GPT-5.5	Mistral Large
Provider	OpenAI	Mistral AI
Noometry Index	63.4	31.9
Released	2026-04-23	2024-02-26
Weights	Proprietary	Open
Context window	1.05M	131K
Max output	128K	16K
Input $ / M tokens	$5	$2
Output $ / M tokens	$30	$6
Results tracked	71	51

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Category by category

Coding GPT-5.5 leads

GPT-5.5: 58.2 (#17), Mistral Large: 34.3 (#240)

Coding benchmarks
Benchmark	GPT-5.5	Mistral Large
SciCode	56.1%	36.2%
LMArena Coding	1494	1277
ALE-Bench	1,943	264.7
SWE-bench Verified	80.6%	—
DeepSWE	67%	—
FrontierCode	43%	—
LMArena WebDev	1513	—
GSO	40.2%	—
WeirdML	84.9%	—
BigCodeBench Instruct	—	30%
LiveBench Coding	—	47.1%
MirrorCode	10%	—
BigCodeBench Complete	—	38.3%
HumanEval+	—	62.2%
MBPP+	—	59.5%

Agentic & Tool Use GPT-5.5 leads

GPT-5.5: 50.7 (#6), Mistral Large: 28.6 (#89)

Agentic & Tool Use benchmarks
Benchmark	GPT-5.5	Mistral Large
Terminal-Bench	84.7%	—
APEX-Agents	55.1%	—
Berkeley Function Calling Leaderboard	—	38.4%
OSWorld 2.0	13%	—
Remote Labor Index	6.3%	—
τ²-bench Banking	44.6%	—
DeepResearch Bench	54%	—
PostTrainBench	27.2%	—
ExploitBench	47.4%	—
GBAEval	53.2%	—
GDP.pdf	26%	—
LMArena Search	1242	—
Vending-Bench 2	7,524	—

Reasoning GPT-5.5 leads

GPT-5.5: 72.8 (#11), Mistral Large: 15.8 (#310)

Reasoning benchmarks
Benchmark	GPT-5.5	Mistral Large
SimpleBench	69%	22.5%
CritPt	27.1%	0%
LMArena Hard Prompts	1489	1257
DTBench	96%	65.1%
LMCA	54.3%	16.7%
Epoch Capabilities Index	159.1	128.52
ForecastBench	60.6	57.1
ARC-AGI-2	85%	—
Kagi LLM Benchmark	88.8%	—
NYT Connections (extended)	96.2%	—
ARC-AGI-1	95%	—
Chess Puzzles	54%	—
EBR-Bench	34.3%	—
LiveBench Reasoning	—	43.5%
Mystery Game Puzzles	56%	—
LiveBench Data Analysis	—	50.1%
Surface Evolver Bench	88.1%	—
Bench to the Future 3	0.14	—
LiveBench	—	48.4%

Math GPT-5.5 leads

GPT-5.5: 81.7 (#11), Mistral Large: 18.2 (#291)

Math benchmarks
Benchmark	GPT-5.5	Mistral Large
OTIS Mock AIME 2024-2025	100%	8.5%
LMArena Math	1486	1262
FrontierMath (Feb 2025 set)	51.7%	0.3%
FrontierMath (Tiers 1-3)	85.3%	—
FrontierMath Tier 4	72.5%	—
MathArena Final-Answer Competitions	94.3%	—
ProofBench	50%	—
Omni-MATH	—	28.1%
LiveBench Math	—	42.5%
MATH Level 5	—	50.3%
FrontierMath Erdős	0%	—
FrontierMath Tier 4 (v1)	35.4%	—

Knowledge GPT-5.5 leads

GPT-5.5: 64.4 (#17), Mistral Large: 30.1 (#230)

Knowledge benchmarks
Benchmark	GPT-5.5	Mistral Large
GPQA Diamond	94%	51.3%
Vectara Hallucination Rate	9.3%	4.5%
LMArena Expert	1508	1232
SimpleQA Verified	63%	—
MMLU-Pro	—	59.9%
Confabulations	—	21.4%
GPQA (HELM)	—	43.5%
MMLU	—	80%

Multimodal Not comparable

GPT-5.5: 46.9 (#12), Mistral Large: —

Multimodal benchmarks
Benchmark	GPT-5.5	Mistral Large
LMArena Vision	1297	—
Blueprint-Bench 2	36.2%	—
Furniture Assembly	44.2%	—
LMArena Document	1486	—

Multilingual GPT-5.5 leads

GPT-5.5: 56.4 (#20), Mistral Large: 40.0 (#219)

Multilingual benchmarks
Benchmark	GPT-5.5	Mistral Large
LMArena Non-English	1467	1237
LMArena Chinese	1533	1240
LMArena French	1486	1325
LMArena German	1480	1254
LMArena Japanese	1498	1188
LMArena Korean	1460	1202
LMArena Russian	1473	1257
LMArena Spanish	1468	1268

Instruction Following GPT-5.5 leads

GPT-5.5: 77.5 (#18), Mistral Large: 67.9 (#191)

Instruction Following benchmarks
Benchmark	GPT-5.5	Mistral Large
LMArena Instruction Following	1479	1249
LiveBench Instruction Following	—	67.9%
IFEval	—	87.7%

Long Context GPT-5.5 leads

GPT-5.5: 48.3 (#12), Mistral Large: 38.3 (#199)

Long Context benchmarks
Benchmark	GPT-5.5	Mistral Large
LMArena Longer Query	1484	1261
CL-bench Life	22.2%	—

Writing & Preference GPT-5.5 leads

GPT-5.5: 72.7 (#13), Mistral Large: 40.7 (#242)

Writing & Preference benchmarks
Benchmark	GPT-5.5	Mistral Large
LMArena Text	1472	1266
LMArena Creative Writing	1455	1243
EQ-Bench Creative Writing	1844	985
LMArena Multi-Turn	1476	1260
Short-Story Creative Writing	—	69%
WildBench	—	80.1%
EQ-Bench 4	1315	—
LiveBench Language	—	39.4%

Frequently asked questions

Is GPT-5.5 better than Mistral Large?

Which is cheaper, GPT-5.5 or Mistral Large?

Mistral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-5.5 lists at $5 and $30.

Is GPT-5.5 or Mistral Large better for coding?

GPT-5.5 scores higher on coding benchmarks: 58.2 versus 34.3 in the Noometry coding category.

Which has the bigger context window?

GPT-5.5 does, with 1.05M tokens against 131K.

How many benchmarks do GPT-5.5 and Mistral Large share?

30 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Mistral Large has 51.