GPT-5.2 vs Mercury 2
GPT-5.2 leads Mercury 2 on the Artificial Analysis Intelligence Index, 30.4 to 10.8, and Mercury 2 costs 18.7x less per output token ($0.75 vs $14.00 per million). Updated October 22, 2026; 38 benchmarks have a result for both.
GPT-5.2 or Mercury 2: which to pick
Only GPT-5.2 has a coding score, 55.9 on the Vibe Coding Index; Mercury 2 has none, so the two cannot be ranked on coding.
Pick Mercury 2 for output tokens 18.7x cheaper ($0.75 vs $14.00 per million).
- Shared results
- 38
- GPT-5.2 only
- 76
- Mercury 2 only
- 4
- 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
Only GPT-5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 against 10.8.
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-5.2 has a GPQA Diamond result, 91.7%.
Directional only
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.2 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
GPT-5.2
GPT-5.2 leads, 1195 against 1036.
Same board, both measured
Long documents
Listed context window
GPT-5.2
GPT-5.2 takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
GPT-5.2 $0.0088, Mercury 2 $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- GPT-5.2 55.9 from 3 benchmarks, Mercury 2 not benchmarked.
- Widest category
- Coding, 21.7 points apart on average over 7 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5.2 82.7% against Mercury 2 43.7%, 39.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Mercury 2 $0.75 per million output tokens, 18.7x apart.
- Context window
- GPT-5.2 takes 400K 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-5.2 | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Knowledge | 3 | 0 | ||
| Web design | 8 | 0 | ||
| General | 13 | 0 | ||
| All shared boards | 38 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
| Benchmark | GPT-5.2 | Mercury 2 | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2 measured |
| SWE-bench Multilingual | 66.7% | Not measured | Only GPT-5.2 measured |
| LMArena WebDev | 1417 | 1171 | GPT-5.2 |
| AA Coding Index | Not measured | 31.1 | Only Mercury 2 measured |
| LiveCodeBench (Vals AI) | 85.4% | Not measured | Only GPT-5.2 measured |
| SWE-bench Verified (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| Vibe Code Bench (Vals AI) | 53.5% | Not measured | Only GPT-5.2 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| SciCode | Not measured | 37.7% | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | 26.5% | GPT-5.2 |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 measured |
| GSO-Bench | 27.5% | Not measured | Only GPT-5.2 measured |
| WeirdML | 72.2% | 43.2% | GPT-5.2 |
| ALE-Bench | 1293.5 | 785.6 | GPT-5.2 |
| LMArena Text: coding | 1442 | 1390 | GPT-5.2 |
| LMArena Text: software and IT | 1434 | 1385 | GPT-5.2 |
| LMArena WebDev: React | Not measured | 1160 | Only Mercury 2 measured |
| LMArena WebDev: HTML | 1427 | 1199 | GPT-5.2 |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2 | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 10.8 | GPT-5.2 |
| Epoch Capabilities Index | 153.7 | Not measured | Only GPT-5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2 | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| Design Arena: agents building web apps | 1086 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | 1043 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | 1093 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1059 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| τ²-Bench Telecom | 84.8% | 70.8% | GPT-5.2 |
| METR time horizons | 75.3% | Not measured | Only GPT-5.2 measured |
| Remote Labor Index | 2.5% | Not measured | Only GPT-5.2 measured |
| GDPval | 49.7% | Not measured | Only GPT-5.2 measured |
| DeepResearch Bench | 41.1% | Not measured | Only GPT-5.2 measured |
| Vending-Bench 2 | $3,591 | Not measured | Only GPT-5.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 55.9% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.2 | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.2 measured |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | 77.0% | GPT-5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | 17.1% | GPT-5.2 |
| GPQA Diamond (Epoch AI run) | 91.4% | Not measured | Only GPT-5.2 measured |
| ARC-AGI | 86.2% | Not measured | Only GPT-5.2 measured |
| ARC-AGI-2 | 52.9% | Not measured | Only GPT-5.2 measured |
| SimpleBench | 45.8% | Not measured | Only GPT-5.2 measured |
| Chess puzzles | 48.0% | Not measured | Only GPT-5.2 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5.2 measured |
| EBR-bench | 23.0% | Not measured | Only GPT-5.2 measured |
| VPCT | 84.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1428 | 1362 | GPT-5.2 |
| LMArena Text: expert | 1445 | 1358 | GPT-5.2 |
| LMArena Text: hard prompts in English | 1431 | 1374 | GPT-5.2 |
| LMArena Vision: diagrams | 1265 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: homework | 1276 | Not measured | Only GPT-5.2 measured |
| Kagi LLM Benchmark | 73.3% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.2 | Mercury 2 | Leader |
|---|---|---|---|
| AIME | 96.9% | Not measured | Only GPT-5.2 measured |
| MGSM (Vals AI) | 94.0% | Not measured | Only GPT-5.2 measured |
| AA Math Index | 99.0 | Not measured | Only GPT-5.2 measured |
| AIME 2025 (Artificial Analysis run) | 99.0% | Not measured | Only GPT-5.2 measured |
| OTIS Mock AIME | 96.1% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tier 4 | 31.7% | Not measured | Only GPT-5.2 measured |
| ProofBench | 15.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: math | 1440 | Not measured | Only GPT-5.2 measured |
| LMArena Text: mathematical work | 1442 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.2 | Mercury 2 | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | Not measured | Only GPT-5.2 measured |
| LegalBench (Vals AI) | 82.8% | Not measured | Only GPT-5.2 measured |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | Not measured | Only GPT-5.2 measured |
| TaxEval (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| MortgageTax (Vals AI) | 67.1% | Not measured | Only GPT-5.2 measured |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | Not measured | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| SimpleQA Verified | 37.1% | Not measured | Only GPT-5.2 measured |
| LMArena Text: science | 1425 | 1343 | GPT-5.2 |
| LMArena Text: medicine and healthcare | 1425 | 1363 | GPT-5.2 |
| LMArena Text: legal and government | 1427 | Not measured | Only GPT-5.2 measured |
| LMArena Text: business and finance | 1414 | 1349 | GPT-5.2 |
| FACTS Benchmark Suite | 48.5% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.2 | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1195 | 1036 | GPT-5.2 |
| Design Arena: UI components | 1190 | 996 | GPT-5.2 |
| Design Arena: game dev | 1195 | 984 | GPT-5.2 |
| Design Arena: data viz | 1202 | 1009 | GPT-5.2 |
| Design Arena: 3D | 1079 | 982 | GPT-5.2 |
| Design Arena: all code categories | 1170 | 1021 | GPT-5.2 |
| Design Arena: SVG | 1139 | 998 | GPT-5.2 |
| Design Arena: ASCII art | 1152 | 988 | GPT-5.2 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | GPT-5.2 | Mercury 2 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 49.3% | Not measured | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.2 measured |
| IFBench | 75.4% | 69.8% | GPT-5.2 |
| Long context reasoning (AA-LCR) | 82.7% | 43.7% | GPT-5.2 |
| DTBench | 90.9% | Not measured | Only GPT-5.2 measured |
| LMCA | 43.9% | Not measured | Only GPT-5.2 measured |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | 1354 | GPT-5.2 |
| LMArena Text: instruction following | 1409 | 1328 | GPT-5.2 |
| LMArena Text: creative writing | 1376 | 1286 | GPT-5.2 |
| LMArena Vision | 1246 | Not measured | Only GPT-5.2 measured |
| LMArena Text: English | 1422 | 1378 | GPT-5.2 |
| LMArena Text: non-English | 1405 | 1330 | GPT-5.2 |
| LMArena Text: Chinese | 1447 | 1414 | GPT-5.2 |
| LMArena Text: German | 1444 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Japanese | 1420 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Korean | 1374 | Not measured | Only GPT-5.2 measured |
| LMArena Text: French | 1454 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Spanish | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Russian | 1415 | 1304 | GPT-5.2 |
| LMArena Text: Polish | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: multi-turn | 1419 | 1357 | GPT-5.2 |
| LMArena Text: longer queries | 1413 | 1329 | GPT-5.2 |
| LMArena Text: writing and language | 1392 | 1300 | GPT-5.2 |
| LMArena Text: entertainment and media | 1378 | 1314 | GPT-5.2 |
| LMArena Vision: English | 1243 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: Chinese | 1305 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: reading text | 1257 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: humor | 1227 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: captioning | 1267 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1225 | Not measured | Only GPT-5.2 measured |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | 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
Mercury 2 costs less
Repository review
50K input + 3K output tokens
Mercury 2 costs less
Agent loop
200K input + 10K output tokens
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-5.2 | Inception Mercury 2 |
|---|---|---|
| Maker | OpenAI | Inception |
| Weights | No public weights listed | No public weights listed |
| Released | December 10, 2025 | March 4, 2026 |
| Context window | 400K | 128K |
| Input $/Mtok | $1.75 | $0.25 |
| Output $/Mtok | $14.00 | $0.75 |
| Coding index | 55.9 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Mercury 2 for coding?
Only GPT-5.2 has a coding score, 55.9 on the Vibe Coding Index; Mercury 2 has none, so the two cannot be ranked on coding. Pick Mercury 2 for output tokens 18.7x cheaper ($0.75 vs $14.00 per million).
Which is cheaper, GPT-5.2 or Mercury 2?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 or Mercury 2?
GPT-5.2, with 400K tokens against 128K.
How many benchmarks is the GPT-5.2 vs Mercury 2 comparison based on?
38 benchmarks have a result for both models as of October 22, 2026. GPT-5.2's coding index rests on 3 independent benchmarks and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.