Model comparison
gpt-oss-120b vs Mercury 2.5
gpt-oss-120b is the stronger model overall, scoring 36.3 to 33.5 on the Noometry Index.
Last verified . 3 shared benchmarks.
Summary
- They share 3 benchmarks with published results for both. gpt-oss-120b scores higher in 1 category and Mercury 2.5 in 2 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 23.3.
- Both cost about the same: $0.037 input and $0.17 output per million tokens.
- Mercury 2.5 accepts more context: 260K tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-120b | Mercury 2.5 | |
|---|---|---|
| Provider | OpenAI | Inception |
| Noometry Index | 36.3 | 33.5 |
| Released | 2025-08-05 | 2026-09-08 |
| Weights | Open | Proprietary |
| Context window | 131K | 260K |
| Max output | 41K | 66K |
| Input $ / M tokens | $0.037 | $0.04 |
| Output $ / M tokens | $0.17 | $0.15 |
| Results tracked | 48 | 4 |
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Category by category
Coding Mercury 2.5 leads
gpt-oss-120b: 33.5 (#256), Mercury 2.5: 39.5 (#156)
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| SciCode | 36% | 38.5% |
| ALE-Bench | 575.62 | 301.65 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| WeirdML | 48.2% | — |
| LMArena Coding | 1380 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.2 (#153), Mercury 2.5: —
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning Mercury 2.5 leads
gpt-oss-120b: 20.0 (#245), Mercury 2.5: 22.4 (#193)
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| CritPt | 1.1% | 0% |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| Chess Puzzles | 20% | — |
| LMArena Hard Prompts | 1364 | — |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.93 | — |
Math gpt-oss-120b leads
gpt-oss-120b: 52.5 (#50), Mercury 2.5: 23.3 (#272)
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| ProofBench | — | 3% |
| Omni-MATH | 68.8% | — |
| LMArena Math | 1389 | — |
Knowledge Not comparable
gpt-oss-120b: 42.4 (#96), Mercury 2.5: —
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
| LMArena Expert | 1356 | — |
Multilingual Not comparable
gpt-oss-120b: 48.0 (#147), Mercury 2.5: —
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| LMArena Non-English | 1351 | — |
| LMArena Chinese | 1385 | — |
| LMArena French | 1369 | — |
| LMArena German | 1353 | — |
| LMArena Japanese | 1331 | — |
| LMArena Korean | 1282 | — |
| LMArena Russian | 1343 | — |
| LMArena Spanish | 1389 | — |
Instruction Following Not comparable
gpt-oss-120b: 69.3 (#173), Mercury 2.5: —
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| IFEval | 83.6% | — |
| LMArena Instruction Following | 1318 | — |
Long Context Not comparable
gpt-oss-120b: 31.4 (#278), Mercury 2.5: —
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| Fiction.LiveBench | 44.4% | — |
| LMArena Longer Query | 1319 | — |
Writing & Preference Not comparable
gpt-oss-120b: 46.5 (#217), Mercury 2.5: —
| Benchmark | gpt-oss-120b | Mercury 2.5 |
|---|---|---|
| LMArena Text | 1365 | — |
| LMArena Creative Writing | 1275 | — |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
| LMArena Multi-Turn | 1340 | — |
Frequently asked questions
Is gpt-oss-120b better than Mercury 2.5?
gpt-oss-120b is the stronger model overall, scoring 36.3 to 33.5 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Mercury 2.5?
Mercury 2.5 is cheaper. It lists at $0.04 per million input tokens and $0.15 per million output tokens; gpt-oss-120b lists at $0.037 and $0.17.
Is gpt-oss-120b or Mercury 2.5 better for coding?
Mercury 2.5 scores higher on coding benchmarks: 39.5 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
Mercury 2.5 does, with 260K tokens against 131K.
How many benchmarks do gpt-oss-120b and Mercury 2.5 share?
3 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mercury 2.5 has 4.