gpt-oss-20b vs MiniMax M2.1
MiniMax M2.1 leads gpt-oss-20b on the Artificial Analysis Intelligence Index, 20.9 to 10.0, and gpt-oss-20b costs 8.0x less per output token ($0.15 vs $1.20 per million). Updated October 23, 2026; 26 benchmarks have a result for both.
gpt-oss-20b or MiniMax M2.1: which to pick
Neither model has a coding score yet, 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 LiveCodeBench (Artificial Analysis run), where it leads.
- Shared results
- 26
- gpt-oss-20b only
- 37
- MiniMax M2.1 only
- 8
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
MiniMax M2.1
MiniMax M2.1 leads, 20.9 against 10.0.
Same board, both measured
Reasoning
GPQA Diamond
MiniMax M2.1
MiniMax M2.1 leads, 78.5% against 68.9%.
Same board, both measured
Math
AIME
gpt-oss-20b
gpt-oss-20b leads, 86.0% against 77.9%.
Same board, both measured
Knowledge
MMLU Pro
MiniMax M2.1
MiniMax M2.1 leads, 87.0% against 71.6%.
Same board, both measured
Web design
Design Arena: websites
MiniMax M2.1
MiniMax M2.1 leads, 1201 against 852.
Same board, both measured
Long documents
Listed context window
MiniMax M2.1
MiniMax M2.1 takes 205K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
gpt-oss-20b $0.0001, MiniMax M2.1 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2.1 has the number, $0.59.
Directional only
At a glance
- Coding index
- gpt-oss-20b not benchmarked, MiniMax M2.1 not benchmarked.
- Widest category
- Web design, 38.9 points apart on average over 2 shared benchmarks.
- Widest single gap
- Design Arena: websites: gpt-oss-20b 852 against MiniMax M2.1 1201, 43.6 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-20b $0.15 against MiniMax M2.1 $1.20 per million output tokens, 8.0x apart.
- Context window
- gpt-oss-20b takes 131K tokens, MiniMax M2.1 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.
| Category | gpt-oss-20b | Bars | MiniMax M2.1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 5 | 0 | ||
| Knowledge | 0 | 7 | ||
| Web design | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 5 | 21 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 80.4% | 81.8% | MiniMax M2.1 |
| SWE-bench Verified (Vals AI) | Not measured | 74.8% | Only MiniMax M2.1 measured |
| LiveCodeBench (Artificial Analysis run) | 77.7% | 81.0% | MiniMax M2.1 |
| SciCode | 38.9% | Not measured | Only gpt-oss-20b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 10.6% | 28.8% | MiniMax M2.1 |
| Terminal-Bench | 3.4% | 36.6% | MiniMax M2.1 |
| WeirdML | 40.9% | Not measured | Only gpt-oss-20b measured |
| ALE-Bench | 566.0 | 623.8 | MiniMax M2.1 |
| LMArena Text: coding | 1306 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: software and IT | 1311 | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.0 | 20.9 | MiniMax M2.1 |
| Epoch Capabilities Index | 137.7 | Not measured | Only gpt-oss-20b measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 60.2% | 85.4% | MiniMax M2.1 |
| BrowseComp-Plus | 50.6% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 68.9% | 78.5% | MiniMax M2.1 |
| GPQA Diamond (Artificial Analysis run) | 68.8% | 83.0% | MiniMax M2.1 |
| Humanity's Last Exam (Artificial Analysis run) | 11.0% | 23.2% | MiniMax M2.1 |
| GPQA Diamond (Epoch AI run) | 60.8% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: hard prompts | 1274 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: expert | 1258 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: hard prompts in English | 1292 | Not measured | Only gpt-oss-20b measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| AIME | 86.0% | 77.9% | gpt-oss-20b |
| MATH 500 (Vals AI) | 94.2% | 89.0% | gpt-oss-20b |
| MGSM (Vals AI) | 89.0% | 87.8% | gpt-oss-20b |
| AA Math Index | 89.3 | 82.7 | gpt-oss-20b |
| AIME 2025 (Artificial Analysis run) | 89.3% | 82.7% | gpt-oss-20b |
| OTIS Mock AIME | 65.3% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: math | 1317 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: mathematical work | 1305 | Not measured | Only gpt-oss-20b measured |
| MathArena | 12.8% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| MMLU Pro | 71.6% | 87.0% | MiniMax M2.1 |
| LegalBench (Vals AI) | 70.8% | 75.5% | MiniMax M2.1 |
| CaseLaw (Vals AI) | 43.8% | 55.8% | MiniMax M2.1 |
| CorpFin (Vals AI) | 53.1% | 58.9% | MiniMax M2.1 |
| TaxEval (Vals AI) | 63.7% | 66.3% | MiniMax M2.1 |
| MedQA (Vals AI) | 82.9% | 91.2% | MiniMax M2.1 |
| MedCode (Vals AI) | Not measured | 34.1% | Only MiniMax M2.1 measured |
| MMLU Pro (Artificial Analysis run) | 74.8% | 87.5% | MiniMax M2.1 |
| LMArena Text: science | 1296 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: medicine and healthcare | 1310 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: legal and government | 1280 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: business and finance | 1283 | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 852 | 1201 | MiniMax M2.1 |
| Design Arena: UI components | Not measured | 1220 | Only MiniMax M2.1 measured |
| Design Arena: game dev | Not measured | 1134 | Only MiniMax M2.1 measured |
| Design Arena: data viz | 933 | 1207 | MiniMax M2.1 |
| Design Arena: 3D | Not measured | 1166 | Only MiniMax M2.1 measured |
| Design Arena: all code categories | Not measured | 1190 | Only MiniMax M2.1 measured |
| Design Arena: SVG | Not measured | 1131 | Only MiniMax M2.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 80.8% | Only MiniMax M2.1 measured |
| IFBench | 65.1% | 69.9% | MiniMax M2.1 |
| Long context reasoning (AA-LCR) | 34.7% | 67.7% | MiniMax M2.1 |
| DTBench | 68.0% | Not measured | Only gpt-oss-20b measured |
| LMCA | 14.5% | Not measured | Only gpt-oss-20b measured |
| LMArena Text | 1287 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: instruction following | 1236 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: creative writing | 1201 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: English | 1303 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: non-English | 1268 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Chinese | 1315 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: German | 1255 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Japanese | 1244 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Korean | 1234 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Spanish | 1267 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Russian | 1278 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Polish | 1252 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: multi-turn | 1268 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: longer queries | 1250 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: writing and language | 1238 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: entertainment and media | 1216 | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-20b | MiniMax M2.1 | Leader |
|---|---|---|---|
| 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 costs less
Repository review
50K input + 3K output tokens
gpt-oss-20b costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | OpenAI gpt-oss-20b | MiniMax M2.1 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | Open weights | Open weights |
| Released | August 5, 2025 | December 23, 2025 |
| Context window | 131K | 205K |
| Input $/Mtok | $0.03 | $0.30 |
| Output $/Mtok | $0.15 | $1.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-20b better than MiniMax M2.1 for coding?
Neither model has a coding score yet, 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 LiveCodeBench (Artificial Analysis run), where it leads.
Which is cheaper, gpt-oss-20b or MiniMax M2.1?
gpt-oss-20b costs $0.03 per million input tokens and $0.15 per million output tokens; MiniMax M2.1 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.1 only.
Which has the bigger context window, gpt-oss-20b or MiniMax M2.1?
MiniMax M2.1, with 205K tokens against 131K.
How many benchmarks is the gpt-oss-20b vs MiniMax M2.1 comparison based on?
26 benchmarks have a result for both models as of October 23, 2026. gpt-oss-20b's coding index rests on 0 independent benchmarks and MiniMax M2.1's on 1. Where only one model has a result, the page says so instead of naming a winner.