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gpt-oss-20b vs Mistral Medium 3.5

Mistral Medium 3.5 leads gpt-oss-20b on the Artificial Analysis Intelligence Index, 14.2 to 10.0, and gpt-oss-20b costs 50.0x less per output token ($0.15 vs $7.50 per million). Updated October 21, 2026; 45 benchmarks have a result for both.

Replace gpt-oss-20b Replace Mistral Medium 3.5
gpt-oss-20bOpenAI
Coding
No data
Overall
10.0
Output $/M
$0.15
1
Mistral Medium 3.5Mistral AI
Coding
25.5
Overall
14.2
Output $/M
$7.50
44
vs boards won of 45 shared

gpt-oss-20b or Mistral Medium 3.5: which to pick

Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; gpt-oss-20b has none, so the two cannot be ranked on coding.

Pick gpt-oss-20b for output tokens 50.0x cheaper ($0.15 vs $7.50 per million) and work measured by GPQA Diamond, where it leads.

Model Agpt-oss-20bOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMistral Medium 3.5Mistral AI25.5/1003 independent benchmarks$6.02 per solved task
Shared results
45
gpt-oss-20b only
15
Mistral Medium 3.5 only
35
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-oss-20b not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
Widest category
Agentic work, 33.9 points apart on average over 1 shared benchmark.
Widest single gap
Design Arena: websites: gpt-oss-20b 852 against Mistral Medium 3.5 1142, 36.3 points apart on a 0 to 100 scale.
Output price
gpt-oss-20b $0.15 against Mistral Medium 3.5 $7.50 per million output tokens, 50.0x apart.
Context window
gpt-oss-20b takes 131K tokens, Mistral Medium 3.5 262K.

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-oss-20bBarsMistral Medium 3.5Ties
Coding05
Intelligence02
Agentic work01
Reasoning15
Math02
Knowledge08
Web design02
General019
All shared boards144
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
LMArena WebDevNot measured1264Only Mistral Medium 3.5 measured
AA Coding IndexNot measured46.9Only Mistral Medium 3.5 measured
LiveCodeBench (Vals AI)80.4%Not measuredOnly gpt-oss-20b measured
SWE-bench Verified (Vals AI)Not measured66.4%Only Mistral Medium 3.5 measured
Vibe Code Bench (Vals AI)Not measured2.9%Only Mistral Medium 3.5 measured
Code migration (Vals AI)Not measured5.1%Only Mistral Medium 3.5 measured
Terminal-Bench 2.1 (Vals AI)Not measured39.0%Only Mistral Medium 3.5 measured
LiveCodeBench (Artificial Analysis run)77.7%Not measuredOnly gpt-oss-20b measured
SciCode38.9%40.2%Mistral Medium 3.5
Terminal-Bench Hard (Artificial Analysis run)10.6%33.3%Mistral Medium 3.5
Terminal-Bench3.4%Not measuredOnly gpt-oss-20b measured
WeirdML40.9%Not measuredOnly gpt-oss-20b measured
ALE-Bench566.0764.0Mistral Medium 3.5
LMArena Text: coding13071460Mistral Medium 3.5
LMArena Text: software and IT13111458Mistral Medium 3.5
LMArena WebDev: ReactNot measured1254Only Mistral Medium 3.5 measured
LMArena WebDev: HTMLNot measured1308Only Mistral Medium 3.5 measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
AA Intelligence Index10.014.2Mistral Medium 3.5
Epoch Capabilities Index137.7141.4Mistral Medium 3.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
AA Agentic IndexNot measured7.0Only Mistral Medium 3.5 measured
Finance Agent (Vals AI)Not measured32.1%Only Mistral Medium 3.5 measured
Tax Agent Bench (Vals AI)Not measured4.5%Only Mistral Medium 3.5 measured
Legal Research Bench (Vals AI)Not measured9.1%Only Mistral Medium 3.5 measured
Excel Modeling Benchmark (Vals AI)Not measured11.2%Only Mistral Medium 3.5 measured
τ²-Bench Telecom60.2%94.2%Mistral Medium 3.5
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
Reasoning benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
GPQA Diamond68.9%34.9%gpt-oss-20b
GPQA Diamond (Artificial Analysis run)68.8%74.8%Mistral Medium 3.5
Humanity's Last Exam (Artificial Analysis run)11.0%13.8%Mistral Medium 3.5
GPQA Diamond (Epoch AI run)60.8%Not measuredOnly gpt-oss-20b measured
LMArena Text: hard prompts12741436Mistral Medium 3.5
LMArena Text: expert12581432Mistral Medium 3.5
LMArena Text: hard prompts in English12921445Mistral Medium 3.5
LMArena Vision: diagramsNot measured1254Only Mistral Medium 3.5 measured
LMArena Vision: homeworkNot measured1230Only Mistral Medium 3.5 measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
AIME86.0%Not measuredOnly gpt-oss-20b measured
MATH 500 (Vals AI)94.2%Not measuredOnly gpt-oss-20b measured
MGSM (Vals AI)89.0%Not measuredOnly gpt-oss-20b measured
AA Math Index89.3Not measuredOnly gpt-oss-20b measured
AIME 2025 (Artificial Analysis run)89.3%Not measuredOnly gpt-oss-20b measured
OTIS Mock AIME65.3%Not measuredOnly gpt-oss-20b measured
ProofBenchNot measured9.0%Only Mistral Medium 3.5 measured
LMArena Text: math13171431Mistral Medium 3.5
LMArena Text: mathematical work13051440Mistral Medium 3.5
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index08
Knowledge benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
MMLU Pro71.6%75.3%Mistral Medium 3.5
LegalBench (Vals AI)70.8%Not measuredOnly gpt-oss-20b measured
CaseLaw (Vals AI)43.8%44.2%Mistral Medium 3.5
CorpFin (Vals AI)53.1%58.8%Mistral Medium 3.5
TaxEval (Vals AI)63.7%68.0%Mistral Medium 3.5
MortgageTax (Vals AI)Not measured28.9%Only Mistral Medium 3.5 measured
MedQA (Vals AI)82.9%Not measuredOnly gpt-oss-20b measured
MedCode (Vals AI)Not measured33.8%Only Mistral Medium 3.5 measured
MMLU Pro (Artificial Analysis run)74.8%Not measuredOnly gpt-oss-20b measured
LMArena Text: science12961428Mistral Medium 3.5
LMArena Text: medicine and healthcare13101409Mistral Medium 3.5
LMArena Text: legal and government12801437Mistral Medium 3.5
LMArena Text: business and finance12841426Mistral Medium 3.5
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index02
Web design benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
Design Arena: websites8521142Mistral Medium 3.5
Design Arena: UI componentsNot measured1079Only Mistral Medium 3.5 measured
Design Arena: game devNot measured1079Only Mistral Medium 3.5 measured
Design Arena: data viz9331120Mistral Medium 3.5
Design Arena: 3DNot measured1061Only Mistral Medium 3.5 measured
Design Arena: all code categoriesNot measured1121Only Mistral Medium 3.5 measured
Design Arena: SVGNot measured950Only Mistral Medium 3.5 measured
Design Arena: ASCII artNot measured1063Only Mistral Medium 3.5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
SAGE (Vals AI)Not measured37.6%Only Mistral Medium 3.5 measured
MedScribe (Vals AI)Not measured67.7%Only Mistral Medium 3.5 measured
Vals Multimodal IndexNot measured34.8%Only Mistral Medium 3.5 measured
IFBench65.1%68.8%Mistral Medium 3.5
Long context reasoning (AA-LCR)34.7%69.3%Mistral Medium 3.5
DTBench68.0%75.5%Mistral Medium 3.5
LMCA14.5%26.1%Mistral Medium 3.5
Surface Evolver BenchNot measured26.9%Only Mistral Medium 3.5 measured
LMArena Text12881421Mistral Medium 3.5
LMArena Text: instruction following12361415Mistral Medium 3.5
LMArena Text: creative writing12011374Mistral Medium 3.5
LMArena VisionNot measured1222Only Mistral Medium 3.5 measured
LMArena Text: English13031433Mistral Medium 3.5
LMArena Text: non-English12691404Mistral Medium 3.5
LMArena Text: Chinese13151444Mistral Medium 3.5
LMArena Text: German12551453Mistral Medium 3.5
LMArena Text: Japanese1244Not measuredOnly gpt-oss-20b measured
LMArena Text: Korean12361384Mistral Medium 3.5
LMArena Text: FrenchNot measured1448Only Mistral Medium 3.5 measured
LMArena Text: Spanish12671411Mistral Medium 3.5
LMArena Text: Russian12781395Mistral Medium 3.5
LMArena Text: Polish12521430Mistral Medium 3.5
LMArena Text: multi-turn12681423Mistral Medium 3.5
LMArena Text: longer queries12501415Mistral Medium 3.5
LMArena Text: writing and language12381384Mistral Medium 3.5
LMArena Text: entertainment and media12161382Mistral Medium 3.5
LMArena Vision: EnglishNot measured1240Only Mistral Medium 3.5 measured
LMArena Vision: ChineseNot measured1244Only Mistral Medium 3.5 measured
LMArena Vision: reading textNot measured1233Only Mistral Medium 3.5 measured
LMArena Vision: humorNot measured1216Only Mistral Medium 3.5 measured
LMArena Vision: creative writingNot measured1198Only Mistral Medium 3.5 measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for gpt-oss-20b and Mistral Medium 3.5
Benchmarkgpt-oss-20bMistral Medium 3.5Leader
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-oss-20b$0.0001Fits in one request
Mistral Medium 3.5$0.0053Fits in one request

gpt-oss-20b costs less

Repository review

50K input + 3K output tokens

gpt-oss-20b$0.0019Fits in one request
Mistral Medium 3.5$0.098Fits in one request

gpt-oss-20b costs less

Agent loop

200K input + 10K output tokens

gpt-oss-20b$0.0075Exceeds the context window
Mistral Medium 3.5$0.375Fits in one request

gpt-oss-20b 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-oss-20b and Mistral Medium 3.5
SpecificationOpenAI gpt-oss-20bMistral Medium 3.5
MakerOpenAIMistral AI
WeightsOpen weightsNo public weights listed
ReleasedAugust 5, 2025April 30, 2026
Context window131K262K
Input $/Mtok$0.03$1.50
Output $/Mtok$0.15$7.50
Coding indexNot benchmarked25.5
Independent measurements behind it03
Cost per solved taskNot computable$6.02
SourceOpenRouter listingOpenRouter listing

Questions

Is gpt-oss-20b better than Mistral Medium 3.5 for coding?

Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; gpt-oss-20b has none, so the two cannot be ranked on coding. Pick gpt-oss-20b for output tokens 50.0x cheaper ($0.15 vs $7.50 per million) and work measured by GPQA Diamond, where it leads.

Which is cheaper, gpt-oss-20b or Mistral Medium 3.5?

gpt-oss-20b costs $0.03 per million input tokens and $0.15 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.

Which has the bigger context window, gpt-oss-20b or Mistral Medium 3.5?

Mistral Medium 3.5, with 262K tokens against 131K.

How many benchmarks is the gpt-oss-20b vs Mistral Medium 3.5 comparison based on?

45 benchmarks have a result for both models as of October 21, 2026. gpt-oss-20b's coding index rests on 0 independent benchmarks and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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