GPT-5.3-Codex vs Mercury 2
GPT-5.3-Codex leads Mercury 2 on the Artificial Analysis Intelligence Index, 32.5 to 10.8, and Mercury 2 costs 18.7x less per output token ($0.75 vs $14.00 per million). Updated October 22, 2026; 20 benchmarks have a result for both.
GPT-5.3-Codex or Mercury 2: which to pick
Only GPT-5.3-Codex has a coding score, 60.0 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
- 20
- GPT-5.3-Codex only
- 14
- Mercury 2 only
- 22
- Like-for-like categories
- 6 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-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 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
Web design
Design Arena: websites
GPT-5.3-Codex
GPT-5.3-Codex leads, 1162 against 1036.
Same board, both measured
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
GPT-5.3-Codex $0.0088, Mercury 2 $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, Mercury 2 not benchmarked.
- Widest category
- Coding, 32.9 points apart on average over 6 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5.3-Codex 83.3% against Mercury 2 43.7%, 39.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Mercury 2 $0.75 per million output tokens, 18.7x apart.
- Context window
- GPT-5.3-Codex 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-5.3-Codex | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 8 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 20 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | GPT-5.3-Codex | Mercury 2 | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | 1171 | GPT-5.3-Codex |
| AA Coding Index | Not measured | 31.1 | Only Mercury 2 measured |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | 61.8% | Not measured | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| SciCode | Not measured | 37.7% | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | 26.5% | GPT-5.3-Codex |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | 43.2% | GPT-5.3-Codex |
| ALE-Bench | 1655.2 | 785.6 | GPT-5.3-Codex |
| LMArena Text: coding | Not measured | 1390 | Only Mercury 2 measured |
| LMArena Text: software and IT | Not measured | 1385 | Only Mercury 2 measured |
| LMArena WebDev: React | 1395 | 1160 | GPT-5.3-Codex |
| LMArena WebDev: HTML | 1422 | 1199 | GPT-5.3-Codex |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 10.8 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | Not measured | Only GPT-5.3-Codex measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1053 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 86.0% | 70.8% | GPT-5.3-Codex |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | 77.0% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 17.1% | GPT-5.3-Codex |
| LMArena Text: hard prompts | Not measured | 1362 | Only Mercury 2 measured |
| LMArena Text: expert | Not measured | 1358 | Only Mercury 2 measured |
| LMArena Text: hard prompts in English | Not measured | 1374 | Only Mercury 2 measured |
| Neither model has a result on the other 17 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Mercury 2 | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.3-Codex | Mercury 2 | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1343 | Only Mercury 2 measured |
| LMArena Text: medicine and healthcare | Not measured | 1363 | Only Mercury 2 measured |
| LMArena Text: business and finance | Not measured | 1349 | Only Mercury 2 measured |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.3-Codex | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | 1036 | GPT-5.3-Codex |
| Design Arena: UI components | 1134 | 996 | GPT-5.3-Codex |
| Design Arena: game dev | 1161 | 984 | GPT-5.3-Codex |
| Design Arena: data viz | 1164 | 1009 | GPT-5.3-Codex |
| Design Arena: 3D | 1007 | 982 | GPT-5.3-Codex |
| Design Arena: all code categories | 1144 | 1021 | GPT-5.3-Codex |
| Design Arena: SVG | 1129 | 998 | GPT-5.3-Codex |
| Design Arena: ASCII art | 1152 | 988 | GPT-5.3-Codex |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | Mercury 2 | Leader |
|---|---|---|---|
| IFBench | 75.4% | 69.8% | GPT-5.3-Codex |
| Long context reasoning (AA-LCR) | 83.3% | 43.7% | GPT-5.3-Codex |
| LMArena Text | Not measured | 1354 | Only Mercury 2 measured |
| LMArena Text: instruction following | Not measured | 1328 | Only Mercury 2 measured |
| LMArena Text: creative writing | Not measured | 1286 | Only Mercury 2 measured |
| LMArena Text: English | Not measured | 1378 | Only Mercury 2 measured |
| LMArena Text: non-English | Not measured | 1330 | Only Mercury 2 measured |
| LMArena Text: Chinese | Not measured | 1414 | Only Mercury 2 measured |
| LMArena Text: Russian | Not measured | 1304 | Only Mercury 2 measured |
| LMArena Text: multi-turn | Not measured | 1357 | Only Mercury 2 measured |
| LMArena Text: longer queries | Not measured | 1329 | Only Mercury 2 measured |
| LMArena Text: writing and language | Not measured | 1300 | Only Mercury 2 measured |
| LMArena Text: entertainment and media | Not measured | 1314 | Only Mercury 2 measured |
| Neither model has a result on the other 33 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | 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-5.3-Codex | Inception Mercury 2 |
|---|---|---|
| Maker | OpenAI | Inception |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | March 4, 2026 |
| Context window | 400K | 128K |
| Input $/Mtok | $1.75 | $0.25 |
| Output $/Mtok | $14.00 | $0.75 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Mercury 2 for coding?
Only GPT-5.3-Codex has a coding score, 60.0 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-5.3-Codex or Mercury 2?
GPT-5.3-Codex 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-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Mercury 2?
GPT-5.3-Codex, with 400K tokens against 128K.
How many benchmarks is the GPT-5.3-Codex vs Mercury 2 comparison based on?
20 benchmarks have a result for both models as of October 22, 2026. GPT-5.3-Codex'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.