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GPT-5 Nano vs Mistral Medium 3.1

GPT-5 Nano leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 13.0 to 9.2, and GPT-5 Nano costs 5.0x less per output token ($0.40 vs $2.00 per million). Updated October 22, 2026; 63 benchmarks have a result for both.

Replace GPT-5 Nano Replace Mistral Medium 3.1
GPT-5 NanoOpenAI
Coding
No data
Overall
35.6
Output $/M
$0.40
17
Mistral Medium 3.1Mistral AI
Coding
No data
Overall
No data
Output $/M
$2.00
46
vs boards won of 63 shared

GPT-5 Nano or Mistral Medium 3.1: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GPT-5 Nano for output tokens 5.0x cheaper ($0.40 vs $2.00 per million) and a larger context window (400K against 131K tokens).

Model AGPT-5 NanoOpenAINot benchmarkedNo coding benchmark$0.109 per solved taskModel BMistral Medium 3.1Mistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
63
GPT-5 Nano only
24
Mistral Medium 3.1 only
11
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-5 Nano not benchmarked, Mistral Medium 3.1 not benchmarked.
Widest category
Math, 23.9 points apart on average over 7 shared benchmarks.
Widest single gap
AA Math Index: GPT-5 Nano 83.7 against Mistral Medium 3.1 38.3, 45.4 points apart on a 0 to 100 scale.
Output price
GPT-5 Nano $0.40 against Mistral Medium 3.1 $2.00 per million output tokens, 5.0x apart.
Context window
GPT-5 Nano takes 400K tokens, Mistral Medium 3.1 131K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-5 NanoBarsMistral Medium 3.1Ties
Coding32
Intelligence10
Agentic work01
Reasoning35
Math43
Knowledge46
Web design06
General223
All shared boards1746
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
Coding benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
SWE-bench Verified34.8%Not measuredOnly GPT-5 Nano measured
AA Coding IndexNot measured20.5Only Mistral Medium 3.1 measured
LiveCodeBench (Vals AI)70.2%44.8%GPT-5 Nano
LiveCodeBench (Artificial Analysis run)78.9%40.6%GPT-5 Nano
SciCodeNot measured32.4%Only Mistral Medium 3.1 measured
Terminal-Bench Hard (Artificial Analysis run)17.4%10.6%GPT-5 Nano
Terminal-Bench21.8%Not measuredOnly GPT-5 Nano measured
WeirdML38.1%Not measuredOnly GPT-5 Nano measured
ALE-BenchNot measured210.2Only Mistral Medium 3.1 measured
LMArena Text: coding13511433Mistral Medium 3.1
LMArena Text: software and IT13451436Mistral Medium 3.1
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
AA Intelligence Index13.09.2GPT-5 Nano
Epoch Capabilities Index139.2Not measuredOnly GPT-5 Nano measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
AA Agentic IndexNot measured1.9Only Mistral Medium 3.1 measured
τ²-Bench Telecom36.5%40.6%Mistral Medium 3.1
Berkeley Function Calling Leaderboard (BFCL)51.4%Not measuredOnly GPT-5 Nano measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index35
Reasoning benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
GPQA Diamond63.4%Not measuredOnly GPT-5 Nano measured
MMMU Pro (Vals AI)70.9%63.0%GPT-5 Nano
GPQA Diamond (Artificial Analysis run)67.6%58.8%GPT-5 Nano
Humanity's Last Exam (Artificial Analysis run)9.5%4.7%GPT-5 Nano
GPQA Diamond (Epoch AI run)69.4%Not measuredOnly GPT-5 Nano measured
ARC-AGI20.7%Not measuredOnly GPT-5 Nano measured
ARC-AGI-22.6%Not measuredOnly GPT-5 Nano measured
Chess puzzles22.0%Not measuredOnly GPT-5 Nano measured
Mystery game puzzles9.0%Not measuredOnly GPT-5 Nano measured
VPCT37.2%Not measuredOnly GPT-5 Nano measured
LMArena Text: hard prompts13281426Mistral Medium 3.1
LMArena Text: expert13211408Mistral Medium 3.1
LMArena Text: hard prompts in English13381435Mistral Medium 3.1
LMArena Vision: diagrams11411175Mistral Medium 3.1
LMArena Vision: homework11571165Mistral Medium 3.1
Kagi LLM Benchmark62.2%Not measuredOnly GPT-5 Nano measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index43
Math benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
AIME81.2%42.3%GPT-5 Nano
MATH 500 (Vals AI)93.8%87.0%GPT-5 Nano
MGSM (Vals AI)89.3%90.9%Mistral Medium 3.1
AA Math Index83.738.3GPT-5 Nano
AIME 2025 (Artificial Analysis run)83.7%38.3%GPT-5 Nano
OTIS Mock AIME81.1%Not measuredOnly GPT-5 Nano measured
FrontierMath, tiers 1 to 320.0%Not measuredOnly GPT-5 Nano measured
FrontierMath, tier 42.4%Not measuredOnly GPT-5 Nano measured
ProofBench12.0%Not measuredOnly GPT-5 Nano measured
MATH level 595.2%Not measuredOnly GPT-5 Nano measured
LMArena Text: math13171408Mistral Medium 3.1
LMArena Text: mathematical work13261412Mistral Medium 3.1
Neither model has a result on the other 1 board here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
Knowledge benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
MMLU Pro76.1%75.3%GPT-5 Nano
LegalBench (Vals AI)50.1%62.0%Mistral Medium 3.1
CaseLaw (Vals AI)52.6%Not measuredOnly GPT-5 Nano measured
CorpFin (Vals AI)Not measured50.7%Only Mistral Medium 3.1 measured
TaxEval (Vals AI)67.4%70.3%Mistral Medium 3.1
MortgageTax (Vals AI)53.6%36.5%GPT-5 Nano
MedQA (Vals AI)93.3%78.2%GPT-5 Nano
MedCode (Vals AI)30.4%Not measuredOnly GPT-5 Nano measured
MMLU Pro (Artificial Analysis run)78.0%68.3%GPT-5 Nano
SimpleQA Verified11.7%Not measuredOnly GPT-5 Nano measured
LMArena Text: science13181444Mistral Medium 3.1
LMArena Text: medicine and healthcare13351449Mistral Medium 3.1
LMArena Text: legal and government12981439Mistral Medium 3.1
LMArena Text: business and finance13111418Mistral Medium 3.1
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
Design Arena: websites11011132Mistral Medium 3.1
Design Arena: UI components10641097Mistral Medium 3.1
Design Arena: game dev10441073Mistral Medium 3.1
Design Arena: data viz10561144Mistral Medium 3.1
Design Arena: 3D9651081Mistral Medium 3.1
Design Arena: all code categories10841120Mistral Medium 3.1
Design Arena: SVGNot measured992Only Mistral Medium 3.1 measured
Design Arena: ASCII artNot measured997Only Mistral Medium 3.1 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index223
General benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
SAGE (Vals AI)30.4%Not measuredOnly GPT-5 Nano measured
MedScribe (Vals AI)72.9%Not measuredOnly GPT-5 Nano measured
IFBench67.6%39.8%GPT-5 Nano
Long context reasoning (AA-LCR)45.0%42.7%GPT-5 Nano
DTBench62.7%67.5%Mistral Medium 3.1
LMCA7.9%19.1%Mistral Medium 3.1
Fiction.LiveBench44.4%Not measuredOnly GPT-5 Nano measured
LMArena Text13201424Mistral Medium 3.1
LMArena Text: instruction following13061398Mistral Medium 3.1
LMArena Text: creative writing12491391Mistral Medium 3.1
LMArena Vision11591172Mistral Medium 3.1
LMArena Text: English13211438Mistral Medium 3.1
LMArena Text: non-English13131408Mistral Medium 3.1
LMArena Text: Chinese13561447Mistral Medium 3.1
LMArena Text: German13271432Mistral Medium 3.1
LMArena Text: Japanese12271379Mistral Medium 3.1
LMArena Text: Korean12691380Mistral Medium 3.1
LMArena Text: FrenchNot measured1460Only Mistral Medium 3.1 measured
LMArena Text: Spanish13601433Mistral Medium 3.1
LMArena Text: Russian12961411Mistral Medium 3.1
LMArena Text: Polish13141430Mistral Medium 3.1
LMArena Text: multi-turn13111418Mistral Medium 3.1
LMArena Text: longer queries13121406Mistral Medium 3.1
LMArena Text: writing and language12831395Mistral Medium 3.1
LMArena Text: entertainment and media12681390Mistral Medium 3.1
LMArena Vision: English11701179Mistral Medium 3.1
LMArena Vision: Chinese11761190Mistral Medium 3.1
LMArena Vision: reading text11521175Mistral Medium 3.1
LMArena Vision: humor11101168Mistral Medium 3.1
LMArena Vision: captioningNot measured1145Only Mistral Medium 3.1 measured
LMArena Vision: recognizing thingsNot measured1147Only Mistral Medium 3.1 measured
LMArena Vision: creative writingNot measured1200Only Mistral Medium 3.1 measured
Neither model has a result on the other 14 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5 Nano and Mistral Medium 3.1
BenchmarkGPT-5 NanoMistral Medium 3.1Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-5 Nano$0.0003Fits in one request
Mistral Medium 3.1$0.0014Fits in one request

GPT-5 Nano costs less

Repository review

50K input + 3K output tokens

GPT-5 Nano$0.0037Fits in one request
Mistral Medium 3.1$0.026Fits in one request

GPT-5 Nano costs less

Agent loop

200K input + 10K output tokens

GPT-5 Nano$0.014Fits in one request
Mistral Medium 3.1$0.100Exceeds the context window

GPT-5 Nano costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-5 Nano and Mistral Medium 3.1
SpecificationOpenAI GPT-5 NanoMistral Medium 3.1
MakerOpenAIMistral AI
WeightsNo public weights listedNo public weights listed
ReleasedAugust 7, 2025August 13, 2025
Context window400K131K
Input $/Mtok$0.05$0.40
Output $/Mtok$0.40$2.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved task$0.109Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5 Nano better than Mistral Medium 3.1 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 5.0x cheaper ($0.40 vs $2.00 per million) and a larger context window (400K against 131K tokens).

Which is cheaper, GPT-5 Nano or Mistral Medium 3.1?

GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for GPT-5 Nano only.

Which has the bigger context window, GPT-5 Nano or Mistral Medium 3.1?

GPT-5 Nano, with 400K tokens against 131K.

How many benchmarks is the GPT-5 Nano vs Mistral Medium 3.1 comparison based on?

63 benchmarks have a result for both models as of October 22, 2026. GPT-5 Nano's coding index rests on 0 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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