DeepSeek V3 vs gpt-oss-120b
gpt-oss-120b costs 1.9x less per output token than DeepSeek V3 ($0.50 vs $0.9594 per million). Updated October 22, 2026; 12 benchmarks have a result for both.
DeepSeek V3 or gpt-oss-120b: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3 for a larger context window (164K against 131K tokens) and work measured by GPQA Diamond (Epoch AI run) or Kagi LLM Benchmark, where it leads.
- Shared results
- 12
- DeepSeek V3 only
- 2
- gpt-oss-120b only
- 69
- Like-for-like categories
- 5 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
No call
Only gpt-oss-120b has a AA Intelligence Index result, 11.6.
Directional only
Agentic work
AA Agentic Index
No call
Only gpt-oss-120b has a AA Agentic Index result, 3.7.
Directional only
Reasoning
GPQA Diamond
No call
Only gpt-oss-120b has a GPQA Diamond result, 78.5%.
Directional only
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-120b has a MMLU Pro result, 79.2%.
Directional only
Web design
Design Arena: websites
DeepSeek V3
DeepSeek V3 leads, 1120 against 967.
Same board, both measured
Long documents
Listed context window
DeepSeek V3
DeepSeek V3 takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
DeepSeek V3 $0.0008, gpt-oss-120b $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only gpt-oss-120b has the number, $0.51.
Directional only
At a glance
- Coding index
- DeepSeek V3 not benchmarked, gpt-oss-120b not benchmarked.
- Widest category
- Math, 40.0 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: DeepSeek V3 48.9% against gpt-oss-120b 88.9%, 40.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 $0.9594 against gpt-oss-120b $0.50 per million output tokens, 1.9x apart.
- Context window
- DeepSeek V3 takes 164K tokens, gpt-oss-120b 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.
| Category | DeepSeek V3 | Bars | gpt-oss-120b | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 1 | ||
| Web design | 6 | 0 | ||
| General | 0 | 2 | ||
| All shared boards | 7 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | DeepSeek V3 | gpt-oss-120b | Leader |
|---|---|---|---|
| Aider polyglot | 70.2% | 41.8% | DeepSeek V3 |
| SWE-bench Verified | Not measured | 26.0% | Only gpt-oss-120b measured |
| SWE-bench Lite | 30.7% | Not measured | Only DeepSeek V3 measured |
| AA Coding Index | Not measured | 30.4 | Only gpt-oss-120b measured |
| LiveCodeBench (Vals AI) | Not measured | 83.2% | Only gpt-oss-120b measured |
| SWE-bench Verified (Vals AI) | Not measured | 33.6% | Only gpt-oss-120b measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 87.8% | Only gpt-oss-120b measured |
| SciCode | Not measured | 34.0% | Only gpt-oss-120b measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 23.5% | Only gpt-oss-120b measured |
| Terminal-Bench | Not measured | 18.7% | Only gpt-oss-120b measured |
| WeirdML | Not measured | 48.2% | Only gpt-oss-120b measured |
| ALE-Bench | Not measured | 575.6 | Only gpt-oss-120b measured |
| LMArena Text: coding | Not measured | 1379 | Only gpt-oss-120b measured |
| LMArena Text: software and IT | Not measured | 1387 | Only gpt-oss-120b measured |
| SciCode | Not measured | 7.7% | Only gpt-oss-120b measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3 | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 11.6 | Only gpt-oss-120b measured |
| Epoch Capabilities Index | Not measured | 139.8 | Only gpt-oss-120b measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V3 | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 3.7 | Only gpt-oss-120b measured |
| τ²-Bench Telecom | Not measured | 65.8% | Only gpt-oss-120b measured |
| APEX-Agents | Not measured | 4.4% | Only gpt-oss-120b measured |
| METR time horizons | Not measured | 56.6% | Only gpt-oss-120b measured |
| Vending-Bench 2 | Not measured | -$22 | Only gpt-oss-120b measured |
| BrowseComp-Plus | Not measured | 67.0% | Only gpt-oss-120b measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3 | gpt-oss-120b | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 78.5% | Only gpt-oss-120b measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 78.2% | Only gpt-oss-120b measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 19.6% | Only gpt-oss-120b measured |
| GPQA Diamond (Epoch AI run) | 71.2% | 75.8% | gpt-oss-120b |
| SimpleBench | Not measured | 22.1% | Only gpt-oss-120b measured |
| Mystery game puzzles | Not measured | 2.0% | Only gpt-oss-120b measured |
| LMArena Text: hard prompts | Not measured | 1364 | Only gpt-oss-120b measured |
| LMArena Text: expert | Not measured | 1356 | Only gpt-oss-120b measured |
| LMArena Text: hard prompts in English | Not measured | 1368 | Only gpt-oss-120b measured |
| Kagi LLM Benchmark | 52.3% | 58.6% | gpt-oss-120b |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 | gpt-oss-120b | Leader |
|---|---|---|---|
| AIME | Not measured | 92.6% | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only gpt-oss-120b measured |
| MGSM (Vals AI) | Not measured | 92.0% | Only gpt-oss-120b measured |
| AA Math Index | Not measured | 93.4 | Only gpt-oss-120b measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 93.4% | Only gpt-oss-120b measured |
| OTIS Mock AIME | 48.9% | 88.9% | gpt-oss-120b |
| LMArena Text: math | Not measured | 1389 | Only gpt-oss-120b measured |
| LMArena Text: mathematical work | Not measured | 1386 | Only gpt-oss-120b measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V3 | gpt-oss-120b | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 79.2% | Only gpt-oss-120b measured |
| LegalBench (Vals AI) | Not measured | 75.9% | Only gpt-oss-120b measured |
| CaseLaw (Vals AI) | Not measured | 48.8% | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | Not measured | 58.2% | Only gpt-oss-120b measured |
| TaxEval (Vals AI) | Not measured | 71.6% | Only gpt-oss-120b measured |
| MedQA (Vals AI) | Not measured | 91.4% | Only gpt-oss-120b measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 80.8% | Only gpt-oss-120b measured |
| LMArena Text: science | Not measured | 1382 | Only gpt-oss-120b measured |
| LMArena Text: medicine and healthcare | Not measured | 1388 | Only gpt-oss-120b measured |
| LMArena Text: legal and government | Not measured | 1355 | Only gpt-oss-120b measured |
| LMArena Text: business and finance | Not measured | 1359 | Only gpt-oss-120b measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V3 | gpt-oss-120b | Leader |
|---|---|---|---|
| Design Arena: websites | 1120 | 967 | DeepSeek V3 |
| Design Arena: UI components | 1090 | 924 | DeepSeek V3 |
| Design Arena: game dev | 1053 | 992 | DeepSeek V3 |
| Design Arena: data viz | 1089 | 987 | DeepSeek V3 |
| Design Arena: 3D | 1086 | 901 | DeepSeek V3 |
| Design Arena: all code categories | 1108 | 962 | DeepSeek V3 |
| Design Arena: SVG | 976 | Not measured | Only DeepSeek V3 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 | DeepSeek V3 | gpt-oss-120b | Leader |
|---|---|---|---|
| IFBench | Not measured | 69.0% | Only gpt-oss-120b measured |
| Long context reasoning (AA-LCR) | Not measured | 52.0% | Only gpt-oss-120b measured |
| DTBench | 62.7% | 76.3% | gpt-oss-120b |
| LMCA | 15.2% | 22.1% | gpt-oss-120b |
| Fiction.LiveBench | Not measured | 44.4% | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | Not measured | 77.1% | Only gpt-oss-120b measured |
| Surface Evolver Bench | Not measured | 25.0% | Only gpt-oss-120b measured |
| LMArena Text | Not measured | 1366 | Only gpt-oss-120b measured |
| LMArena Text: instruction following | Not measured | 1318 | Only gpt-oss-120b measured |
| LMArena Text: creative writing | Not measured | 1274 | Only gpt-oss-120b measured |
| LMArena Text: English | Not measured | 1375 | Only gpt-oss-120b measured |
| LMArena Text: non-English | Not measured | 1351 | Only gpt-oss-120b measured |
| LMArena Text: Chinese | Not measured | 1385 | Only gpt-oss-120b measured |
| LMArena Text: German | Not measured | 1353 | Only gpt-oss-120b measured |
| LMArena Text: Japanese | Not measured | 1331 | Only gpt-oss-120b measured |
| LMArena Text: Korean | Not measured | 1282 | Only gpt-oss-120b measured |
| LMArena Text: French | Not measured | 1370 | Only gpt-oss-120b measured |
| LMArena Text: Spanish | Not measured | 1389 | Only gpt-oss-120b measured |
| LMArena Text: Russian | Not measured | 1343 | Only gpt-oss-120b measured |
| LMArena Text: Polish | Not measured | 1353 | Only gpt-oss-120b measured |
| LMArena Text: multi-turn | Not measured | 1340 | Only gpt-oss-120b measured |
| LMArena Text: longer queries | Not measured | 1319 | Only gpt-oss-120b measured |
| LMArena Text: writing and language | Not measured | 1306 | Only gpt-oss-120b measured |
| LMArena Text: entertainment and media | Not measured | 1292 | Only gpt-oss-120b measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 | gpt-oss-120b | 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-120b costs less
Repository review
50K input + 3K output tokens
gpt-oss-120b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-120b 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 | DeepSeek V3 | OpenAI gpt-oss-120b |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | Open weights |
| Released | December 26, 2024 | August 5, 2025 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.2887 | $0.10 |
| Output $/Mtok | $0.9594 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.51 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 better than gpt-oss-120b for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3 for a larger context window (164K against 131K tokens) and work measured by GPQA Diamond (Epoch AI run) or Kagi LLM Benchmark, where it leads.
Which is cheaper, DeepSeek V3 or gpt-oss-120b?
DeepSeek V3 costs $0.2887 per million input tokens and $0.9594 per million output tokens; gpt-oss-120b costs $0.10 and $0.50. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, DeepSeek V3 or gpt-oss-120b?
DeepSeek V3, with 164K tokens against 131K.
How many benchmarks is the DeepSeek V3 vs gpt-oss-120b comparison based on?
12 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3's coding index rests on 0 independent benchmarks and gpt-oss-120b's on 1. Where only one model has a result, the page says so instead of naming a winner.