Model comparison
gpt-oss-120b vs Mercury
Mercury is the stronger model overall, scoring 37.6 to 36.3 on the Noometry Index.
Last verified . 9 shared benchmarks.
Summary
- They share 9 benchmarks with published results for both. gpt-oss-120b scores higher in 4 categories and Mercury in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where Mercury leads 38.4 to 31.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 58.6% for gpt-oss-120b and 21.6% for Mercury.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-120b | Mercury | |
|---|---|---|
| Provider | OpenAI | Inception |
| Noometry Index | 36.3 | 37.6 |
| Released | 2025-08-05 | — |
| Weights | Open | Proprietary |
| Context window | 131K | — |
| Max output | 41K | — |
| Input $ / M tokens | $0.037 | — |
| Output $ / M tokens | $0.17 | — |
| Results tracked | 48 | 9 |
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Category by category
Coding Mercury leads
gpt-oss-120b: 33.5 (#256), Mercury: 38.7 (#170)
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| LMArena Coding | 1380 | 1322 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.2 (#153), Mercury: —
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning gpt-oss-120b leads
gpt-oss-120b: 20.0 (#245), Mercury: 17.5 (#293)
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| Kagi LLM Benchmark | 58.6% | 21.6% |
| LMArena Hard Prompts | 1364 | 1285 |
| SimpleBench | 22.1% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.93 | — |
Math Not comparable
gpt-oss-120b: 52.5 (#50), Mercury: —
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |
| LMArena Math | 1389 | — |
Knowledge Not comparable
gpt-oss-120b: 42.4 (#96), Mercury: —
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
| LMArena Expert | 1356 | — |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.0 (#147), Mercury: 41.6 (#206)
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| LMArena Non-English | 1351 | 1260 |
| LMArena Chinese | 1385 | — |
| LMArena French | 1369 | — |
| LMArena German | 1353 | — |
| LMArena Japanese | 1331 | — |
| LMArena Korean | 1282 | — |
| LMArena Russian | 1343 | — |
| LMArena Spanish | 1389 | — |
Instruction Following gpt-oss-120b leads
gpt-oss-120b: 69.3 (#173), Mercury: 65.2 (#224)
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| LMArena Instruction Following | 1318 | 1239 |
| IFEval | 83.6% | — |
Long Context Mercury leads
gpt-oss-120b: 31.4 (#278), Mercury: 38.4 (#198)
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| LMArena Longer Query | 1319 | 1266 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Too close to call
gpt-oss-120b: 46.5 (#217), Mercury: 46.2 (#221)
| Benchmark | gpt-oss-120b | Mercury |
|---|---|---|
| LMArena Text | 1365 | 1282 |
| LMArena Creative Writing | 1275 | 1191 |
| LMArena Multi-Turn | 1340 | 1282 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than Mercury?
Mercury is the stronger model overall, scoring 37.6 to 36.3 on the Noometry Index.
Is gpt-oss-120b or Mercury better for coding?
Mercury scores higher on coding benchmarks: 38.7 versus 33.5 in the Noometry coding category.
How many benchmarks do gpt-oss-120b and Mercury share?
9 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mercury has 9.