Model comparison
DeepSeek V4 Pro vs gpt-oss-120b
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 36.3 on the Noometry Index. gpt-oss-120b costs 14× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
DeepSeekSummary
- They share 34 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 9 categories and gpt-oss-120b in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 20.0.
- The biggest single-benchmark swing is APEX-Agents: 47.3% for DeepSeek V4 Pro and 4.4% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 131K.
Side by side
| DeepSeek V4 Pro | gpt-oss-120b | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 54.3 | 36.3 |
| Released | 2026-04-24 | 2025-08-05 |
| Weights | Open | Open |
| Context window | 1M | 131K |
| Max output | 393K | 41K |
| Input $ / M tokens | $0.66 | $0.037 |
| Output $ / M tokens | $1.98 | $0.17 |
| Results tracked | 48 | 48 |
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Category by category
Coding DeepSeek V4 Pro leads
DeepSeek V4 Pro: 52.4 (#34), gpt-oss-120b: 33.5 (#256)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| SciCode | 51% | 36% |
| WeirdML | 66.2% | 48.2% |
| LMArena Coding | 1470 | 1380 |
| ALE-Bench | 1,403 | 575.62 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| LMArena WebDev | 1582 | — |
| AlgoTune | — | 1.41 |
Agentic & Tool Use DeepSeek V4 Pro leads
DeepSeek V4 Pro: 32.8 (#58), gpt-oss-120b: 12.2 (#153)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| APEX-Agents | 47.3% | 4.4% |
| Vending-Bench 2 | 3,285 | -21.53 |
| Terminal-Bench | — | 18.7% |
| METR Time Horizons | — | 56.6% |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), gpt-oss-120b: 20.0 (#245)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| Kagi LLM Benchmark | 53.5% | 58.6% |
| CritPt | 18% | 1.1% |
| Chess Puzzles | 47% | 20% |
| LMArena Hard Prompts | 1461 | 1364 |
| Mystery Game Puzzles | 43% | 2% |
| DTBench | 93.9% | 76.3% |
| LMCA | 45.5% | 22.1% |
| Surface Evolver Bench | 40% | 25% |
| Epoch Capabilities Index | 155.31 | 139.93 |
| ARC-AGI-2 | 61.3% | — |
| SimpleBench | — | 22.1% |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| ForecastBench | 56.1 | — |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), gpt-oss-120b: 52.5 (#50)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.6% | 88.9% |
| LMArena Math | 1455 | 1389 |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| ProofBench | 50% | — |
| Omni-MATH | — | 68.8% |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), gpt-oss-120b: 42.4 (#96)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 91.7% | 75.8% |
| Vectara Hallucination Rate | 8.6% | 14.2% |
| LMArena Expert | 1464 | 1356 |
| SimpleQA Verified | 52.9% | — |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| GPQA (HELM) | — | 68.4% |
Multilingual DeepSeek V4 Pro leads
DeepSeek V4 Pro: 54.4 (#45), gpt-oss-120b: 48.0 (#147)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1439 | 1351 |
| LMArena Chinese | 1486 | 1385 |
| LMArena French | 1472 | 1369 |
| LMArena German | 1458 | 1353 |
| LMArena Japanese | 1445 | 1331 |
| LMArena Korean | 1447 | 1282 |
| LMArena Russian | 1453 | 1343 |
| LMArena Spanish | 1458 | 1389 |
Instruction Following DeepSeek V4 Pro leads
DeepSeek V4 Pro: 76.1 (#47), gpt-oss-120b: 69.3 (#173)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1448 | 1318 |
| IFEval | — | 83.6% |
Long Context DeepSeek V4 Pro leads
DeepSeek V4 Pro: 45.0 (#51), gpt-oss-120b: 31.4 (#278)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1458 | 1319 |
| Fiction.LiveBench | — | 44.4% |
| CL-bench Life | 13.5% | — |
Writing & Preference DeepSeek V4 Pro leads
DeepSeek V4 Pro: 65.5 (#46), gpt-oss-120b: 46.5 (#217)
| Benchmark | DeepSeek V4 Pro | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1451 | 1365 |
| LMArena Creative Writing | 1446 | 1275 |
| EQ-Bench Creative Writing | 1553 | 961 |
| LMArena Multi-Turn | 1467 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| WildBench | — | 84.5% |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than gpt-oss-120b?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 36.3 on the Noometry Index. gpt-oss-120b costs 14× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Pro or gpt-oss-120b?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or gpt-oss-120b better for coding?
DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Pro does, with 1M tokens against 131K.
How many benchmarks do DeepSeek V4 Pro and gpt-oss-120b share?
34 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and gpt-oss-120b has 48.