GPT-5.6 Sol vs Mercury 2
Updated October 21, 2026. 42 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 42 shared
Decision reading
Pick GPT-5.6 Sol for coding: it scores 76.7 on the Vibe Coding Index against 0.0 for Mercury 2.
Pick Mercury 2 when output tokens cost 26.7x less.
Model AGPT-5.6 SolOpenAI76.7/1002 independent benchmarks$1.20 per solved taskModel BMercury 2Inception0.0/1001 independent benchmarkNo cost per solved task
- Shared results
- 42
- GPT-5.6 Sol only
- 94
- Mercury 2 only
- 0
- Like-for-like categories
- 7 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, four public coding benchmarks
GPT-5.6 Sol
GPT-5.6 Sol scores 76.7 against 0.0.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 50.2 against 1.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Sol has a GPQA Diamond result, 95.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Sol has a MMLU Pro result, 89.1%.
Directional only
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1036.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
GPT-5.6 Sol $0.014, Mercury 2 $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Sol has the number, $1.20.
Directional only
At a glance
- Coding index
- GPT-5.6 Sol 76.7 from 2 benchmarks, Mercury 2 0.0 from 1 benchmark.
- Widest category
- Coding, 43.2 points apart on average over 10 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GPT-5.6 Sol 1620 against Mercury 2 1160, 73.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Mercury 2 $0.75 per million output tokens, 26.7x apart.
- Context window
- GPT-5.6 Sol takes 1.05M 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-5.6 Sol | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 10 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Knowledge | 3 | 0 | ||
| Web design | 8 | 0 | ||
| General | 13 | 0 | ||
| All shared boards | 42 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index100
| Benchmark | GPT-5.6 Sol | Mercury 2 | Leader |
|---|---|---|---|
| LiveBench coding | 89.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1616 | 1171 | GPT-5.6 Sol |
| AA Coding Index | 77.4 | 31.1 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 82.6% | Not measured | Only GPT-5.6 Sol measured |
| SWE-bench Verified (Vals AI) | 96.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 4 (Vals AI) | 37.9% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench (Vals AI) | 80.5% | Not measured | Only GPT-5.6 Sol measured |
| Code migration (Vals AI) | 52.9% | Not measured | Only GPT-5.6 Sol measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Sol measured |
| IOI (Vals AI) | 91.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | 54.1% | Not measured | Only GPT-5.6 Sol measured |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 57.8% | 37.7% | GPT-5.6 Sol |
| Terminal-Bench Hard (Artificial Analysis run) | 65.9% | 26.5% | GPT-5.6 Sol |
| FrontierCode | 47.5% | Not measured | Only GPT-5.6 Sol measured |
| FrontierSWE | 32.2% | Not measured | Only GPT-5.6 Sol measured |
| GSO-Bench | 76.5% | Not measured | Only GPT-5.6 Sol measured |
| WeirdML | 88.8% | 43.2% | GPT-5.6 Sol |
| MirrorCode | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| ALE-Bench | 2176.9 | 785.6 | GPT-5.6 Sol |
| LMArena Text: coding | 1496 | 1390 | GPT-5.6 Sol |
| LMArena Text: software and IT | 1489 | 1385 | GPT-5.6 Sol |
| LMArena WebDev: React | 1620 | 1160 | GPT-5.6 Sol |
| LMArena WebDev: HTML | 1618 | 1199 | GPT-5.6 Sol |
| LMArena WebDev: from an image | 1609 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | 10.8 | GPT-5.6 Sol |
| Epoch Capabilities Index | 162.8 | Not measured | Only GPT-5.6 Sol measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | 50.2 | 1.6 | GPT-5.6 Sol |
| Design Arena: agents building web apps | 1245 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building full-stack apps | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building mobile apps | 1249 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Android apps | 1246 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Godot games | 1260 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building games | 1267 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | 1286 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: PowerPoint slides | 1240 | Not measured | Only GPT-5.6 Sol measured |
| LiveBench research | 54.5% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench long agentic | 48.5% | Not measured | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 53.8% | Not measured | Only GPT-5.6 Sol measured |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only GPT-5.6 Sol measured |
| Legal Research Bench (Vals AI) | 48.1% | Not measured | Only GPT-5.6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 72.3% | Not measured | Only GPT-5.6 Sol measured |
| BioMysteryBench (Vals AI) | 71.1% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| Vals Science Index | 41.5% | Not measured | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | 23.8% | Not measured | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | 8.3% | Not measured | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 85.1% | 70.8% | GPT-5.6 Sol |
| OSWorld 2.0 | 27.3% | Not measured | Only GPT-5.6 Sol measured |
| BALROG | 60.0% | Not measured | Only GPT-5.6 Sol measured |
| PostTrainBench | 36.2% | Not measured | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | $9,619 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Search | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.6 Sol | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench reasoning | 91.7% | Not measured | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | 33.3% | Not measured | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 94.1% | 77.0% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | 17.1% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI | 97.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | 92.5% | Not measured | Only GPT-5.6 Sol measured |
| SimpleBench | 64.8% | Not measured | Only GPT-5.6 Sol measured |
| Chess puzzles | 59.0% | Not measured | Only GPT-5.6 Sol measured |
| Mystery game puzzles | 58.0% | Not measured | Only GPT-5.6 Sol measured |
| EBR-bench | 44.8% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1483 | 1362 | GPT-5.6 Sol |
| LMArena Text: expert | 1517 | 1358 | GPT-5.6 Sol |
| LMArena Text: hard prompts in English | 1477 | 1374 | GPT-5.6 Sol |
| LMArena Vision: diagrams | 1305 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | 1305 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 3 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.6 Sol | Mercury 2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tiers 1 to 3 | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tier 4 | 82.9% | Not measured | Only GPT-5.6 Sol measured |
| ProofBench | 83.0% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: math | 1473 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: mathematical work | 1496 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Sol | Mercury 2 | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| LegalBench (Vals AI) | 87.0% | Not measured | Only GPT-5.6 Sol measured |
| CorpFin (Vals AI) | 64.4% | Not measured | Only GPT-5.6 Sol measured |
| TaxEval (Vals AI) | 74.8% | Not measured | Only GPT-5.6 Sol measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | 44.0% | Not measured | Only GPT-5.6 Sol measured |
| SimpleQA Verified | 69.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: science | 1455 | 1343 | GPT-5.6 Sol |
| LMArena Text: medicine and healthcare | 1431 | 1363 | GPT-5.6 Sol |
| LMArena Text: legal and government | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: business and finance | 1457 | 1349 | GPT-5.6 Sol |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.6 Sol | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | 1036 | GPT-5.6 Sol |
| Design Arena: UI components | 1338 | 998 | GPT-5.6 Sol |
| Design Arena: game dev | 1368 | 985 | GPT-5.6 Sol |
| Design Arena: data viz | 1323 | 1009 | GPT-5.6 Sol |
| Design Arena: 3D | 1385 | 982 | GPT-5.6 Sol |
| Design Arena: all code categories | 1341 | 1022 | GPT-5.6 Sol |
| Design Arena: SVG | 1316 | 998 | GPT-5.6 Sol |
| Design Arena: ASCII art | 1302 | 990 | GPT-5.6 Sol |
| LiveBench design | 81.6% | Not measured | Only GPT-5.6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | GPT-5.6 Sol | Mercury 2 | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.8% | Not measured | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | 66.5% | Not measured | Only GPT-5.6 Sol measured |
| SAGE (Vals AI) | 52.6% | Not measured | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | 85.2% | Not measured | Only GPT-5.6 Sol measured |
| Vals Multimodal Index | 72.6% | Not measured | Only GPT-5.6 Sol measured |
| IFBench | 72.7% | 69.8% | GPT-5.6 Sol |
| Long context reasoning (AA-LCR) | 84.0% | 43.7% | GPT-5.6 Sol |
| DTBench | 95.7% | Not measured | Only GPT-5.6 Sol measured |
| LMCA | 58.5% | Not measured | Only GPT-5.6 Sol measured |
| Surface Evolver Bench | 93.1% | Not measured | Only GPT-5.6 Sol measured |
| Furniture assembly | 56.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text | 1457 | 1354 | GPT-5.6 Sol |
| LMArena Text: instruction following | 1483 | 1328 | GPT-5.6 Sol |
| LMArena Text: creative writing | 1449 | 1286 | GPT-5.6 Sol |
| LMArena Vision | 1281 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: English | 1457 | 1378 | GPT-5.6 Sol |
| LMArena Text: non-English | 1451 | 1330 | GPT-5.6 Sol |
| LMArena Text: Chinese | 1525 | 1414 | GPT-5.6 Sol |
| LMArena Text: German | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Japanese | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Korean | 1442 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: French | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Spanish | 1443 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Russian | 1471 | 1304 | GPT-5.6 Sol |
| LMArena Text: Polish | 1459 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: multi-turn | 1461 | 1357 | GPT-5.6 Sol |
| LMArena Text: longer queries | 1481 | 1329 | GPT-5.6 Sol |
| LMArena Text: writing and language | 1462 | 1300 | GPT-5.6 Sol |
| LMArena Text: entertainment and media | 1450 | 1314 | GPT-5.6 Sol |
| LMArena Vision: English | 1280 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | 1313 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | 1290 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | 1246 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | 1283 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Document | 1483 | Not measured | Only GPT-5.6 Sol 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 | GPT-5.6 Sol | 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-5.6 Sol$0.014Fits in one request
Mercury 2$0.0006Fits in one request
Mercury 2 costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Sol$0.260Fits in one request
Mercury 2$0.015Fits in one request
Mercury 2 costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Sol$1.00Fits in one request
Mercury 2$0.058Exceeds the context window
Mercury 2 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-5.6 Sol | Inception Mercury 2 |
|---|---|---|
| Maker | OpenAI | Inception |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | March 4, 2026 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $4.00 | $0.25 |
| Output $/Mtok | $20.00 | $0.75 |
| Coding index | 76.7 | 0.0 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $1.20 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Mercury 2 for coding?
Pick GPT-5.6 Sol for coding: it scores 76.7 on the Vibe Coding Index against 0.0 for Mercury 2. Pick Mercury 2 when output tokens cost 26.7x less.
Which is cheaper, GPT-5.6 Sol or Mercury 2?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for GPT-5.6 Sol only.
Which has the bigger context window, GPT-5.6 Sol or Mercury 2?
GPT-5.6 Sol, with 1.05M tokens against 128K.
How many benchmarks is the GPT-5.6 Sol vs Mercury 2 comparison based on?
42 benchmarks have a result for both models as of October 21, 2026. GPT-5.6 Sol's coding index rests on 2 independent benchmarks and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.