GPT-4o (2024-05-13) vs Mercury 2
Mercury 2 leads GPT-4o (2024-05-13) on the Artificial Analysis Intelligence Index, 10.8 to 7.3, and Mercury 2 costs 20.0x less per output token ($0.75 vs $15.00 per million). Updated October 22, 2026; 23 benchmarks have a result for both.
GPT-4o (2024-05-13) or Mercury 2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o (2024-05-13) for work measured by AA Coding Index or LMArena Text: coding, where it leads.
- Shared results
- 23
- GPT-4o (2024-05-13) only
- 21
- Mercury 2 only
- 19
- Like-for-like categories
- 5 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 7.3.
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-4o (2024-05-13) has a GPQA Diamond result, 50.2%.
Directional only
Web design
Design Arena: websites
No call
Only Mercury 2 has a Design Arena: websites result, 1035.
Directional only
Long documents
Listed context window
No call
Both take 128K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
GPT-4o (2024-05-13) $0.013, Mercury 2 $0.0006 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) not benchmarked, Mercury 2 not benchmarked.
- Widest category
- Reasoning, 16.9 points apart on average over 5 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-4o (2024-05-13) 52.6% against Mercury 2 77.0%, 24.4 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-05-13) $15.00 against Mercury 2 $0.75 per million output tokens, 20.0x apart.
- Context window
- Both take 128K tokens.
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-4o (2024-05-13) | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Knowledge | 0 | 3 | ||
| General | 1 | 10 | ||
| All shared boards | 1 | 22 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GPT-4o (2024-05-13) | Mercury 2 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1171 | Only Mercury 2 measured |
| AA Coding Index | 24.2 | 31.1 | Mercury 2 |
| LiveCodeBench (Artificial Analysis run) | 33.4% | Not measured | Only GPT-4o (2024-05-13) measured |
| SciCode | Not measured | 37.7% | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 26.5% | Only Mercury 2 measured |
| WeirdML | Not measured | 43.2% | Only Mercury 2 measured |
| ALE-Bench | Not measured | 785.6 | Only Mercury 2 measured |
| LMArena Text: coding | 1297 | 1390 | Mercury 2 |
| LMArena Text: software and IT | 1297 | 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 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-05-13) | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | 10.8 | Mercury 2 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| τ²-Bench Telecom | Not measured | 70.8% | Only Mercury 2 measured |
| METR time horizons | 40.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| BALROG | 32.3% | Not measured | Only GPT-4o (2024-05-13) 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-4o (2024-05-13) | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 52.6% | 77.0% | Mercury 2 |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | 17.1% | Mercury 2 |
| GPQA Diamond (Epoch AI run) | 48.9% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts | 1281 | 1362 | Mercury 2 |
| LMArena Text: expert | 1250 | 1358 | Mercury 2 |
| LMArena Text: hard prompts in English | 1293 | 1374 | Mercury 2 |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Mercury 2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | 1284 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: mathematical work | 1284 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o (2024-05-13) | Mercury 2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| MMLU | 84.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: science | 1295 | 1343 | Mercury 2 |
| LMArena Text: medicine and healthcare | 1278 | 1363 | Mercury 2 |
| LMArena Text: legal and government | 1314 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: business and finance | 1262 | 1349 | Mercury 2 |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1035 | Only Mercury 2 measured |
| Design Arena: UI components | Not measured | 996 | Only Mercury 2 measured |
| Design Arena: game dev | Not measured | 983 | Only Mercury 2 measured |
| Design Arena: data viz | Not measured | 1010 | Only Mercury 2 measured |
| Design Arena: 3D | Not measured | 982 | Only Mercury 2 measured |
| Design Arena: all code categories | Not measured | 1021 | Only Mercury 2 measured |
| Design Arena: SVG | Not measured | 997 | 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 index110
| Benchmark | GPT-4o (2024-05-13) | Mercury 2 | Leader |
|---|---|---|---|
| IFBench | Not measured | 69.8% | Only Mercury 2 measured |
| Long context reasoning (AA-LCR) | Not measured | 43.7% | Only Mercury 2 measured |
| LMArena Text | 1300 | 1354 | Mercury 2 |
| LMArena Text: instruction following | 1278 | 1328 | Mercury 2 |
| LMArena Text: creative writing | 1291 | 1286 | GPT-4o (2024-05-13) |
| LMArena Vision | 1137 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | 1312 | 1378 | Mercury 2 |
| LMArena Text: non-English | 1283 | 1330 | Mercury 2 |
| LMArena Text: Chinese | 1277 | 1414 | Mercury 2 |
| LMArena Text: German | 1283 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Japanese | 1257 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Korean | 1234 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: French | 1304 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Spanish | 1292 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Russian | 1285 | 1304 | Mercury 2 |
| LMArena Text: Polish | 1365 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: multi-turn | 1302 | 1357 | Mercury 2 |
| LMArena Text: longer queries | 1288 | 1329 | Mercury 2 |
| LMArena Text: writing and language | 1298 | 1300 | Mercury 2 |
| LMArena Text: entertainment and media | 1289 | 1314 | Mercury 2 |
| LMArena Vision: English | 1147 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: Chinese | 1132 | Not measured | Only GPT-4o (2024-05-13) measured |
| 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-4o (2024-05-13) | 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-4o (2024-05-13) | Inception Mercury 2 |
|---|---|---|
| Maker | OpenAI | Inception |
| Weights | No public weights listed | No public weights listed |
| Released | May 13, 2024 | March 4, 2026 |
| Context window | 128K | 128K |
| Input $/Mtok | $5.00 | $0.25 |
| Output $/Mtok | $15.00 | $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-4o (2024-05-13) better than Mercury 2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-05-13) for work measured by AA Coding Index or LMArena Text: coding, where it leads.
Which is cheaper, GPT-4o (2024-05-13) or Mercury 2?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 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-4o (2024-05-13) or Mercury 2?
Neither: both take 128K tokens.
How many benchmarks is the GPT-4o (2024-05-13) vs Mercury 2 comparison based on?
23 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-05-13)'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.