gpt-oss-20b vs Mercury 2
Mercury 2 leads gpt-oss-20b on the Artificial Analysis Intelligence Index, 10.8 to 10.0, and gpt-oss-20b costs 5.0x less per output token ($0.15 vs $0.75 per million). Updated October 21, 2026; 31 benchmarks have a result for both.
gpt-oss-20b or Mercury 2: 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 5.0x cheaper ($0.15 vs $0.75 per million) and a larger context window (131K against 128K tokens).
- Shared results
- 31
- gpt-oss-20b only
- 31
- Mercury 2 only
- 11
- Like-for-like categories
- 7 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
Mercury 2
Mercury 2 leads, 10.8 against 10.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mercury 2 has a AA Agentic Index result, 1.6.
Directional only
Reasoning
GPQA Diamond
No call
Only gpt-oss-20b has a GPQA Diamond result, 68.9%.
Directional only
Math
AIME
No call
Only gpt-oss-20b has a AIME result, 86.0%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-20b has a MMLU Pro result, 71.6%.
Directional only
Web design
Design Arena: websites
Mercury 2
Mercury 2 leads, 1036 against 852.
Same board, both measured
Long documents
Listed context window
gpt-oss-20b
gpt-oss-20b takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
gpt-oss-20b $0.0001, Mercury 2 $0.0006 per turn.
List prices
At a glance
- Coding index
- gpt-oss-20b not benchmarked, Mercury 2 not benchmarked.
- Widest category
- Web design, 16.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- Design Arena: websites: gpt-oss-20b 852 against Mercury 2 1036, 23.0 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-20b $0.15 against Mercury 2 $0.75 per million output tokens, 5.0x apart.
- Context window
- gpt-oss-20b takes 131K tokens, Mercury 2 128K.
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 | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 1 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Knowledge | 0 | 3 | ||
| Web design | 0 | 2 | ||
| General | 0 | 13 | ||
| All shared boards | 1 | 30 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index15
| Benchmark | gpt-oss-20b | Mercury 2 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1171 | Only Mercury 2 measured |
| AA Coding Index | Not measured | 31.1 | Only Mercury 2 measured |
| LiveCodeBench (Vals AI) | 80.4% | Not measured | Only gpt-oss-20b measured |
| LiveCodeBench (Artificial Analysis run) | 77.7% | Not measured | Only gpt-oss-20b measured |
| SciCode | 38.9% | 37.7% | gpt-oss-20b |
| Terminal-Bench Hard (Artificial Analysis run) | 10.6% | 26.5% | Mercury 2 |
| Terminal-Bench | 3.4% | Not measured | Only gpt-oss-20b measured |
| WeirdML | 40.9% | 43.2% | Mercury 2 |
| ALE-Bench | 566.0 | 785.6 | Mercury 2 |
| LMArena Text: coding | 1307 | 1390 | Mercury 2 |
| LMArena Text: software and IT | 1311 | 1385 | Mercury 2 |
| LMArena WebDev: React | Not measured | 1160 | Only Mercury 2 measured |
| LMArena WebDev: HTML | Not measured | 1199 | Only Mercury 2 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | gpt-oss-20b | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.0 | 10.8 | Mercury 2 |
| 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 | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| τ²-Bench Telecom | 60.2% | 70.8% | Mercury 2 |
| BrowseComp-Plus | 50.6% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | gpt-oss-20b | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond | 68.9% | Not measured | Only gpt-oss-20b measured |
| GPQA Diamond (Artificial Analysis run) | 68.8% | 77.0% | Mercury 2 |
| Humanity's Last Exam (Artificial Analysis run) | 11.0% | 17.1% | Mercury 2 |
| GPQA Diamond (Epoch AI run) | 60.8% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: hard prompts | 1274 | 1362 | Mercury 2 |
| LMArena Text: expert | 1258 | 1358 | Mercury 2 |
| LMArena Text: hard prompts in English | 1292 | 1374 | Mercury 2 |
| Kagi LLM Benchmark | 53.2% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | gpt-oss-20b | Mercury 2 | Leader |
|---|---|---|---|
| AIME | 86.0% | Not measured | Only gpt-oss-20b measured |
| MATH 500 (Vals AI) | 94.2% | Not measured | Only gpt-oss-20b measured |
| MGSM (Vals AI) | 89.0% | Not measured | Only gpt-oss-20b measured |
| AA Math Index | 89.3 | Not measured | Only gpt-oss-20b measured |
| AIME 2025 (Artificial Analysis run) | 89.3% | Not measured | Only gpt-oss-20b measured |
| 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 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | gpt-oss-20b | Mercury 2 | Leader |
|---|---|---|---|
| MMLU Pro | 71.6% | Not measured | Only gpt-oss-20b measured |
| LegalBench (Vals AI) | 70.8% | Not measured | Only gpt-oss-20b measured |
| CaseLaw (Vals AI) | 43.8% | Not measured | Only gpt-oss-20b measured |
| CorpFin (Vals AI) | 53.1% | Not measured | Only gpt-oss-20b measured |
| TaxEval (Vals AI) | 63.7% | Not measured | Only gpt-oss-20b measured |
| MedQA (Vals AI) | 82.9% | Not measured | Only gpt-oss-20b measured |
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: science | 1296 | 1343 | Mercury 2 |
| LMArena Text: medicine and healthcare | 1310 | 1363 | Mercury 2 |
| LMArena Text: legal and government | 1280 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: business and finance | 1284 | 1349 | Mercury 2 |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-20b | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 852 | 1036 | Mercury 2 |
| Design Arena: UI components | Not measured | 998 | Only Mercury 2 measured |
| Design Arena: game dev | Not measured | 985 | Only Mercury 2 measured |
| Design Arena: data viz | 933 | 1009 | Mercury 2 |
| Design Arena: 3D | Not measured | 983 | Only Mercury 2 measured |
| Design Arena: all code categories | Not measured | 1022 | Only Mercury 2 measured |
| Design Arena: SVG | Not measured | 998 | Only Mercury 2 measured |
| Design Arena: ASCII art | Not measured | 988 | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index013
| Benchmark | gpt-oss-20b | Mercury 2 | Leader |
|---|---|---|---|
| IFBench | 65.1% | 69.8% | Mercury 2 |
| Long context reasoning (AA-LCR) | 34.7% | 43.7% | Mercury 2 |
| DTBench | 68.0% | Not measured | Only gpt-oss-20b measured |
| LMCA | 14.5% | Not measured | Only gpt-oss-20b measured |
| LMArena Text | 1288 | 1354 | Mercury 2 |
| LMArena Text: instruction following | 1236 | 1328 | Mercury 2 |
| LMArena Text: creative writing | 1201 | 1286 | Mercury 2 |
| LMArena Text: English | 1303 | 1378 | Mercury 2 |
| LMArena Text: non-English | 1269 | 1330 | Mercury 2 |
| LMArena Text: Chinese | 1315 | 1414 | Mercury 2 |
| 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 | 1236 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Spanish | 1267 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: Russian | 1278 | 1304 | Mercury 2 |
| LMArena Text: Polish | 1252 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: multi-turn | 1268 | 1357 | Mercury 2 |
| LMArena Text: longer queries | 1250 | 1329 | Mercury 2 |
| LMArena Text: writing and language | 1238 | 1300 | Mercury 2 |
| LMArena Text: entertainment and media | 1216 | 1314 | Mercury 2 |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-20b | Mercury 2 | 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 | Inception Mercury 2 |
|---|---|---|
| Maker | OpenAI | Inception |
| Weights | Open weights | No public weights listed |
| Released | August 5, 2025 | March 4, 2026 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.03 | $0.25 |
| Output $/Mtok | $0.15 | $0.75 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-20b better than Mercury 2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-20b for output tokens 5.0x cheaper ($0.15 vs $0.75 per million) and a larger context window (131K against 128K tokens).
Which is cheaper, gpt-oss-20b or Mercury 2?
gpt-oss-20b costs $0.03 per million input tokens and $0.15 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for neither of them.
Which has the bigger context window, gpt-oss-20b or Mercury 2?
gpt-oss-20b, with 131K tokens against 128K.
How many benchmarks is the gpt-oss-20b vs Mercury 2 comparison based on?
31 benchmarks have a result for both models as of October 21, 2026. gpt-oss-20b's coding index rests on 0 independent benchmarks and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.