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gpt-oss-20b vs MiniMax M2.7

MiniMax M2.7 leads gpt-oss-20b on the Artificial Analysis Intelligence Index, 22.8 to 10.0, and gpt-oss-20b costs 8.0x less per output token ($0.15 vs $1.20 per million). Updated October 22, 2026; 49 benchmarks have a result for both.

Replace gpt-oss-20b Replace MiniMax M2.7
gpt-oss-20bOpenAI
Coding
No data
Overall
38.5
Output $/M
$0.15
2
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
47
vs boards won of 49 shared

gpt-oss-20b or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 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 8.0x cheaper ($0.15 vs $1.20 per million) and work measured by LiveCodeBench (Vals AI) or WeirdML, where it leads.

Model Agpt-oss-20bOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
49
gpt-oss-20b only
13
MiniMax M2.7 only
25
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, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Web design, 43.2 points apart on average over 2 shared benchmarks.
Widest single gap
Design Arena: websites: gpt-oss-20b 852 against MiniMax M2.7 1243, 48.9 points apart on a 0 to 100 scale.
Output price
gpt-oss-20b $0.15 against MiniMax M2.7 $1.20 per million output tokens, 8.0x apart.
Context window
gpt-oss-20b takes 131K tokens, MiniMax M2.7 205K.

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-20bBarsMiniMax M2.7Ties
Coding26
Intelligence02
Agentic work01
Reasoning06
Math03
Knowledge09
Web design02
General018
All shared boards247
CodingLike-for-likeSix of these boards feed the Vibe Coding Index26
Coding benchmarks for gpt-oss-20b and MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding IndexNot measured52.6Only MiniMax M2.7 measured
LiveCodeBench (Vals AI)80.4%79.9%gpt-oss-20b
SWE-bench Verified (Vals AI)Not measured73.8%Only MiniMax M2.7 measured
Vibe Code Bench (Vals AI)Not measured11.9%Only MiniMax M2.7 measured
Code migration (Vals AI)Not measured8.7%Only MiniMax M2.7 measured
ProgramBench (Vals AI)Not measured0.0%Only MiniMax M2.7 measured
Terminal-Bench 2.1 (Vals AI)Not measured48.7%Only MiniMax M2.7 measured
LiveCodeBench (Artificial Analysis run)77.7%Not measuredOnly gpt-oss-20b measured
SciCode38.9%50.1%MiniMax M2.7
Terminal-Bench Hard (Artificial Analysis run)10.6%39.4%MiniMax M2.7
Terminal-Bench3.4%45.1%MiniMax M2.7
WeirdML40.9%37.0%gpt-oss-20b
ALE-Bench566.0599.3MiniMax M2.7
LMArena Text: coding13071454MiniMax M2.7
LMArena Text: software and IT13111446MiniMax M2.7
LMArena WebDev: ReactNot measured1388Only MiniMax M2.7 measured
LMArena WebDev: HTMLNot measured1401Only MiniMax M2.7 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 MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
AA Intelligence Index10.022.8MiniMax M2.7
Epoch Capabilities Index137.7145.8MiniMax M2.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for gpt-oss-20b and MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
AA Agentic IndexNot measured15.3Only MiniMax M2.7 measured
Finance Agent (Vals AI)Not measured27.9%Only MiniMax M2.7 measured
Tax Agent Bench (Vals AI)Not measured6.9%Only MiniMax M2.7 measured
Legal Research Bench (Vals AI)Not measured10.6%Only MiniMax M2.7 measured
Excel Modeling Benchmark (Vals AI)Not measured28.4%Only MiniMax M2.7 measured
τ²-Bench Telecom60.2%84.8%MiniMax M2.7
BrowseComp-Plus50.6%Not measuredOnly gpt-oss-20b measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for gpt-oss-20b and MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
GPQA Diamond68.9%86.6%MiniMax M2.7
GPQA Diamond (Artificial Analysis run)68.8%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)11.0%29.6%MiniMax M2.7
GPQA Diamond (Epoch AI run)60.8%Not measuredOnly gpt-oss-20b measured
LMArena Text: hard prompts12741422MiniMax M2.7
LMArena Text: expert12581443MiniMax M2.7
LMArena Text: hard prompts in English12921441MiniMax M2.7
Kagi LLM Benchmark53.2%Not measuredOnly gpt-oss-20b measured
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for gpt-oss-20b and MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
AIME86.0%91.0%MiniMax M2.7
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 measured3.0%Only MiniMax M2.7 measured
LMArena Text: math13171421MiniMax M2.7
LMArena Text: mathematical work13051437MiniMax M2.7
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index09
Knowledge benchmarks for gpt-oss-20b and MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
MMLU Pro71.6%80.4%MiniMax M2.7
LegalBench (Vals AI)70.8%84.0%MiniMax M2.7
CaseLaw (Vals AI)43.8%60.9%MiniMax M2.7
CorpFin (Vals AI)53.1%61.2%MiniMax M2.7
TaxEval (Vals AI)63.7%66.6%MiniMax M2.7
MedQA (Vals AI)82.9%Not measuredOnly gpt-oss-20b measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)74.8%Not measuredOnly gpt-oss-20b measured
LMArena Text: science12961416MiniMax M2.7
LMArena Text: medicine and healthcare13101409MiniMax M2.7
LMArena Text: legal and government12801403MiniMax M2.7
LMArena Text: business and finance12841407MiniMax M2.7
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index02
Web design benchmarks for gpt-oss-20b and MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
Design Arena: websites8521243MiniMax M2.7
Design Arena: UI componentsNot measured1210Only MiniMax M2.7 measured
Design Arena: game devNot measured1209Only MiniMax M2.7 measured
Design Arena: data viz9331233MiniMax M2.7
Design Arena: 3DNot measured1195Only MiniMax M2.7 measured
Design Arena: all code categoriesNot measured1233Only MiniMax M2.7 measured
Design Arena: SVGNot measured1137Only MiniMax M2.7 measured
Design Arena: ASCII artNot measured1136Only MiniMax M2.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for gpt-oss-20b and MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench65.1%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)34.7%78.3%MiniMax M2.7
DTBench68.0%Not measuredOnly gpt-oss-20b measured
LMCA14.5%Not measuredOnly gpt-oss-20b measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text12881405MiniMax M2.7
LMArena Text: instruction following12361405MiniMax M2.7
LMArena Text: creative writing12011354MiniMax M2.7
LMArena Text: English13031426MiniMax M2.7
LMArena Text: non-English12691382MiniMax M2.7
LMArena Text: Chinese13151442MiniMax M2.7
LMArena Text: German12551398MiniMax M2.7
LMArena Text: Japanese12441260MiniMax M2.7
LMArena Text: Korean12361314MiniMax M2.7
LMArena Text: FrenchNot measured1424Only MiniMax M2.7 measured
LMArena Text: Spanish12671402MiniMax M2.7
LMArena Text: Russian12781383MiniMax M2.7
LMArena Text: Polish12521387MiniMax M2.7
LMArena Text: multi-turn12681412MiniMax M2.7
LMArena Text: longer queries12501418MiniMax M2.7
LMArena Text: writing and language12381376MiniMax M2.7
LMArena Text: entertainment and media12161360MiniMax M2.7
Neither model has a result on the other 23 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 MiniMax M2.7
Benchmarkgpt-oss-20bMiniMax M2.7Leader
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
MiniMax M2.7$0.0009Fits in one request

gpt-oss-20b costs less

Repository review

50K input + 3K output tokens

gpt-oss-20b$0.0019Fits in one request
MiniMax M2.7$0.019Fits in one request

gpt-oss-20b costs less

Agent loop

200K input + 10K output tokens

gpt-oss-20b$0.0075Exceeds the context window
MiniMax M2.7$0.072Exceeds the context window

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 MiniMax M2.7
SpecificationOpenAI gpt-oss-20bMiniMax M2.7
MakerOpenAIMiniMax
WeightsOpen weightsOpen weights
ReleasedAugust 5, 2025March 18, 2026
Context window131K205K
Input $/Mtok$0.03$0.30
Output $/Mtok$0.15$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is gpt-oss-20b better than MiniMax M2.7 for coding?

Only MiniMax M2.7 has a coding score, 36.2 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 8.0x cheaper ($0.15 vs $1.20 per million) and work measured by LiveCodeBench (Vals AI) or WeirdML, where it leads.

Which is cheaper, gpt-oss-20b or MiniMax M2.7?

gpt-oss-20b costs $0.03 per million input tokens and $0.15 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.7 only.

Which has the bigger context window, gpt-oss-20b or MiniMax M2.7?

MiniMax M2.7, with 205K tokens against 131K.

How many benchmarks is the gpt-oss-20b vs MiniMax M2.7 comparison based on?

49 benchmarks have a result for both models as of October 22, 2026. gpt-oss-20b's coding index rests on 0 independent benchmarks and MiniMax M2.7's on 3. Where only one model has a result, the page says so instead of naming a winner.

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