Mercury 2.5 vs o3
Updated October 21, 2026. 7 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding.
o3 has the edge where what matters is ALE-Bench or AA Intelligence Index, where it leads.
- Shared results
- 7
- Mercury 2.5 only
- 15
- o3 only
- 90
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Only Mercury 2.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
o3
o3 leads, 20.2 against 12.3.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only o3 has a GPQA Diamond result, 84.1%.
Directional only
Math
AIME
No call
Only o3 has a AIME result, 85.3%.
Directional only
Knowledge
MMLU Pro
No call
Only o3 has a MMLU Pro result, 85.6%.
Directional only
Web design
Design Arena: websites
No call
Only o3 has a Design Arena: websites result, 1036.
Directional only
Long documents
Listed context window
Mercury 2.5
Mercury 2.5 takes 260K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Mercury 2.5 $0.0001, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only o3 has the number, $0.571.
Directional only
At a glance
- Coding index
- Mercury 2.5 1.6 from 2 benchmarks, o3 not benchmarked.
- Widest category
- Coding, 21.1 points apart on average over 1 shared benchmark.
- Widest single gap
- MedScribe (Vals AI): Mercury 2.5 55.1% against o3 76.7%, 21.6 points apart on a 0 to 100 scale.
- Output price
- Mercury 2.5 $0.15 against o3 $8.00 per million output tokens, 53.3x apart.
- Context window
- Mercury 2.5 takes 260K tokens, o3 200K.
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 | Mercury 2.5 | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Knowledge | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Mercury 2.5 | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | Not measured | 58.4% | Only o3 measured |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| LiveCodeBench (Vals AI) | Not measured | 83.9% | Only o3 measured |
| Terminal-Bench 4 (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | 3.7% | Not measured | Only Mercury 2.5 measured |
| Code migration (Vals AI) | 4.5% | Not measured | Only Mercury 2.5 measured |
| IOI (Vals AI) | 2.4% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 34.5% | Not measured | Only Mercury 2.5 measured |
| SkillsBench (Vals AI) | 18.1% | Not measured | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | 45.5% | Not measured | Only Mercury 2.5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 80.8% | Only o3 measured |
| SciCode | 38.5% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 37.1% | Only o3 measured |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | Not measured | 52.4% | Only o3 measured |
| ALE-Bench | 301.6 | 933.5 | o3 |
| LMArena Text: coding | Not measured | 1407 | Only o3 measured |
| LMArena Text: software and IT | Not measured | 1415 | Only o3 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2.5 | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.3 | 20.2 | o3 |
| Epoch Capabilities Index | Not measured | 146.7 | Only o3 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | Mercury 2.5 | o3 | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | 18.9% | Not measured | Only Mercury 2.5 measured |
| Tax Agent Bench (Vals AI) | 2.5% | Not measured | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | 4.3% | Not measured | Only Mercury 2.5 measured |
| Excel Modeling Benchmark (Vals AI) | 8.8% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| τ²-Bench Telecom | Not measured | 80.7% | Only o3 measured |
| METR time horizons | Not measured | 65.4% | Only o3 measured |
| GDPval | Not measured | 30.8% | Only o3 measured |
| DeepResearch Bench | Not measured | 45.2% | Only o3 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2.5 | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 84.1% | Only o3 measured |
| MMMU Pro (Vals AI) | Not measured | 80.4% | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 82.7% | Only o3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 11.8% | 20.1% | o3 |
| GPQA Diamond (Epoch AI run) | Not measured | 81.8% | Only o3 measured |
| ARC-AGI | Not measured | 60.8% | Only o3 measured |
| ARC-AGI-2 | Not measured | 6.5% | Only o3 measured |
| SimpleBench | Not measured | 53.1% | Only o3 measured |
| Chess puzzles | Not measured | 37.0% | Only o3 measured |
| Mystery game puzzles | Not measured | 29.0% | Only o3 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | Not measured | 1402 | Only o3 measured |
| LMArena Text: expert | Not measured | 1402 | Only o3 measured |
| LMArena Text: hard prompts in English | Not measured | 1411 | Only o3 measured |
| LMArena Vision: diagrams | Not measured | 1220 | Only o3 measured |
| LMArena Vision: homework | Not measured | 1234 | Only o3 measured |
| Neither model has a result on the other 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Mercury 2.5 | o3 | Leader |
|---|---|---|---|
| AIME | Not measured | 85.3% | Only o3 measured |
| MATH 500 (Vals AI) | Not measured | 94.6% | Only o3 measured |
| MGSM (Vals AI) | Not measured | 91.8% | Only o3 measured |
| AA Math Index | Not measured | 88.3 | Only o3 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 88.3% | Only o3 measured |
| OTIS Mock AIME | Not measured | 84.4% | Only o3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only o3 measured |
| ProofBench | 3.0% | Not measured | Only Mercury 2.5 measured |
| MATH level 5 | Not measured | 97.8% | Only o3 measured |
| LMArena Text: math | Not measured | 1426 | Only o3 measured |
| LMArena Text: mathematical work | Not measured | 1423 | Only o3 measured |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mercury 2.5 | o3 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 85.6% | Only o3 measured |
| LegalBench (Vals AI) | 83.1% | 83.8% | o3 |
| CorpFin (Vals AI) | Not measured | 59.7% | Only o3 measured |
| TaxEval (Vals AI) | Not measured | 74.6% | Only o3 measured |
| MortgageTax (Vals AI) | Not measured | 65.7% | Only o3 measured |
| MedQA (Vals AI) | Not measured | 96.1% | Only o3 measured |
| MedCode (Vals AI) | 31.3% | 47.3% | o3 |
| MMLU Pro (Artificial Analysis run) | Not measured | 85.3% | Only o3 measured |
| SimpleQA Verified | Not measured | 49.4% | Only o3 measured |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | Not measured | 1429 | Only o3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1448 | Only o3 measured |
| LMArena Text: legal and government | Not measured | 1428 | Only o3 measured |
| LMArena Text: business and finance | Not measured | 1393 | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Mercury 2.5 | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1036 | Only o3 measured |
| Design Arena: UI components | Not measured | 1017 | Only o3 measured |
| Design Arena: game dev | Not measured | 1034 | Only o3 measured |
| Design Arena: data viz | Not measured | 1200 | Only o3 measured |
| Design Arena: all code categories | Not measured | 1020 | Only o3 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mercury 2.5 | o3 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 45.3% | Not measured | Only Mercury 2.5 measured |
| SAGE (Vals AI) | Not measured | 41.8% | Only o3 measured |
| MedScribe (Vals AI) | 55.1% | 76.7% | o3 |
| IFBench | Not measured | 71.4% | Only o3 measured |
| Long context reasoning (AA-LCR) | 71.7% | 74.7% | o3 |
| DTBench | Not measured | 84.8% | Only o3 measured |
| LMCA | Not measured | 39.7% | Only o3 measured |
| Fiction.LiveBench | Not measured | 88.9% | Only o3 measured |
| Creative writing (Lech Mazur) | Not measured | 83.9% | Only o3 measured |
| CL-bench | Not measured | 17.8% | Only o3 measured |
| LMArena Text | Not measured | 1410 | Only o3 measured |
| LMArena Text: instruction following | Not measured | 1368 | Only o3 measured |
| LMArena Text: creative writing | Not measured | 1359 | Only o3 measured |
| LMArena Vision | Not measured | 1214 | Only o3 measured |
| LMArena Text: English | Not measured | 1417 | Only o3 measured |
| LMArena Text: non-English | Not measured | 1401 | Only o3 measured |
| LMArena Text: Chinese | Not measured | 1437 | Only o3 measured |
| LMArena Text: German | Not measured | 1420 | Only o3 measured |
| LMArena Text: Japanese | Not measured | 1403 | Only o3 measured |
| LMArena Text: Korean | Not measured | 1370 | Only o3 measured |
| LMArena Text: French | Not measured | 1430 | Only o3 measured |
| LMArena Text: Spanish | Not measured | 1395 | Only o3 measured |
| LMArena Text: Russian | Not measured | 1406 | Only o3 measured |
| LMArena Text: Polish | Not measured | 1438 | Only o3 measured |
| LMArena Text: multi-turn | Not measured | 1405 | Only o3 measured |
| LMArena Text: longer queries | Not measured | 1372 | Only o3 measured |
| LMArena Text: writing and language | Not measured | 1372 | Only o3 measured |
| LMArena Text: entertainment and media | Not measured | 1373 | Only o3 measured |
| LMArena Vision: English | Not measured | 1221 | Only o3 measured |
| LMArena Vision: Chinese | Not measured | 1237 | Only o3 measured |
| LMArena Vision: reading text | Not measured | 1218 | Only o3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only o3 measured |
| LMArena Vision: captioning | Not measured | 1210 | Only o3 measured |
| LMArena Vision: recognizing things | Not measured | 1234 | Only o3 measured |
| LMArena Vision: creative writing | Not measured | 1201 | Only o3 measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | o3 | 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
Mercury 2.5 costs less
Repository review
50K input + 3K output tokens
Mercury 2.5 costs less
Agent loop
200K input + 10K output tokens
Mercury 2.5 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 | Inception Mercury 2.5 | OpenAI o3 |
|---|---|---|
| Maker | Inception | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | September 8, 2026 | April 16, 2025 |
| Context window | 260K | 200K |
| Input $/Mtok | $0.04 | $2.00 |
| Output $/Mtok | $0.15 | $8.00 |
| Coding index | 1.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2.5 better than o3 for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; o3 has none, so the two cannot be ranked on coding. o3 has the edge where what matters is ALE-Bench or AA Intelligence Index, where it leads.
Which is cheaper, Mercury 2.5 or o3?
Mercury 2.5 costs $0.04 per million input tokens and $0.15 per million output tokens; o3 costs $2.00 and $8.00. Cost per solved task is known for o3 only.
Which has the bigger context window, Mercury 2.5 or o3?
Mercury 2.5, with 260K tokens against 200K.
How many benchmarks is the Mercury 2.5 vs o3 comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. Mercury 2.5's coding index rests on 2 independent benchmarks and o3's on 0. Where only one model has a result, the page says so instead of naming a winner.