GPT-6 Luna vs Mercury 2.5
GPT-6 Luna leads Mercury 2.5 on coding, 57.3 to 1.6 on the Vibe Coding Index, and Mercury 2.5 costs 3.3x less per output token ($0.15 vs $0.50 per million). Updated October 23, 2026; 20 benchmarks have a result for both.
vs boards won of 20 shared
GPT-6 Luna or Mercury 2.5: which to pick
Pick GPT-6 Luna for coding: it scores 57.3 on the Vibe Coding Index against 1.6 for Mercury 2.5.
Pick Mercury 2.5 for output tokens 3.3x cheaper ($0.15 vs $0.50 per million).
Model AGPT-6 LunaOpenAI57.3/1005 independent benchmarksNo cost per solved taskModel BMercury 2.5Inception1.6/1002 independent benchmarksNo cost per solved task
- Shared results
- 20
- GPT-6 Luna only
- 84
- Mercury 2.5 only
- 2
- 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
GPT-6 Luna
GPT-6 Luna scores 57.3 against 1.6.
5 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6 Luna
GPT-6 Luna leads, 38.1 against 12.3.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-6 Luna has a Design Arena: websites result, 1270.
Directional only
General
Vals Index
No call
Only GPT-6 Luna has a Vals Index result, 52.3%.
Directional only
Long documents
Listed context window
GPT-6 Luna
GPT-6 Luna takes 1.05M tokens against 260K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
GPT-6 Luna $0.0003, Mercury 2.5 $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-6 Luna 57.3 from 5 benchmarks, Mercury 2.5 1.6 from 2 benchmarks.
- Widest category
- Math, 61.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-6 Luna 81.7% against Mercury 2.5 3.7%, 78.0 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Luna $0.50 against Mercury 2.5 $0.15 per million output tokens, 3.3x apart.
- Context window
- GPT-6 Luna takes 1.05M tokens, Mercury 2.5 260K.
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-6 Luna | Bars | Mercury 2.5 | Ties |
|---|---|---|---|---|
| Coding | 8 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 1 | 0 | ||
| Math | 1 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 20 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index80
| Benchmark | GPT-6 Luna | Mercury 2.5 | Leader |
|---|---|---|---|
| LiveBench coding | 84.5% | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev | 1578 | Not measured | Only GPT-6 Luna measured |
| Terminal-Bench 4 (Vals AI) | 13.6% | 0.0% | GPT-6 Luna |
| Vibe Code Bench (Vals AI) | 81.7% | 3.7% | GPT-6 Luna |
| Code migration (Vals AI) | 42.5% | 4.5% | GPT-6 Luna |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-6 Luna measured |
| IOI (Vals AI) | 55.6% | 2.4% | GPT-6 Luna |
| Terminal-Bench 2.1 (Vals AI) | 73.0% | 34.5% | GPT-6 Luna |
| SWE-bench Multimodal (Vals AI) | 37.5% | Not measured | Only GPT-6 Luna measured |
| SkillsBench (Vals AI) | Not measured | 18.1% | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | 76.3% | 45.5% | GPT-6 Luna |
| SciCode | 54.6% | 38.5% | GPT-6 Luna |
| FrontierCode | 42.4% | Not measured | Only GPT-6 Luna measured |
| ALE-Bench | 1576.9 | 301.6 | GPT-6 Luna |
| LMArena Text: coding | 1438 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: software and IT | 1425 | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev: React | 1574 | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev: HTML | 1595 | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev: from an image | 1571 | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6 Luna | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.1 | 12.3 | GPT-6 Luna |
| Epoch Capabilities Index | 156.8 | Not measured | Only GPT-6 Luna measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-6 Luna | Mercury 2.5 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1191 | Not measured | Only GPT-6 Luna measured |
| Design Arena: agents building full-stack apps | 1158 | Not measured | Only GPT-6 Luna measured |
| Design Arena: agents building mobile apps | 1125 | Not measured | Only GPT-6 Luna measured |
| LiveBench research | 43.5% | Not measured | Only GPT-6 Luna measured |
| LiveBench browser | 72.0% | Not measured | Only GPT-6 Luna measured |
| LiveBench long agentic | 48.1% | Not measured | Only GPT-6 Luna measured |
| SREBench (Vals AI) | 2.7% | Not measured | Only GPT-6 Luna measured |
| Finance Agent (Vals AI) | 49.9% | 18.9% | GPT-6 Luna |
| Tax Agent Bench (Vals AI) | 19.9% | 2.5% | GPT-6 Luna |
| Legal Research Bench (Vals AI) | 30.3% | 4.3% | GPT-6 Luna |
| Harvey's Legal Agent Benchmark (Vals AI) | 4.2% | Not measured | Only GPT-6 Luna measured |
| Excel Modeling Benchmark (Vals AI) | 68.5% | 8.8% | GPT-6 Luna |
| BioMysteryBench (Vals AI) | 61.5% | Not measured | Only GPT-6 Luna measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | 0.0% | GPT-6 Luna |
| Vals Science Index | 28.4% | Not measured | Only GPT-6 Luna measured |
| APEX-Agents | 44.3% | Not measured | Only GPT-6 Luna measured |
| SpireClimb | 0.0% | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6 Luna | Mercury 2.5 | Leader |
|---|---|---|---|
| LiveBench reasoning | 81.8% | Not measured | Only GPT-6 Luna measured |
| MysteryMechanism (Vals AI) | 19.4% | Not measured | Only GPT-6 Luna measured |
| Humanity's Last Exam (Artificial Analysis run) | 38.5% | 11.8% | GPT-6 Luna |
| GPQA Diamond (Epoch AI run) | 90.5% | Not measured | Only GPT-6 Luna measured |
| ARC-AGI | 86.7% | Not measured | Only GPT-6 Luna measured |
| ARC-AGI-2 | 59.3% | Not measured | Only GPT-6 Luna measured |
| Chess puzzles | 36.0% | Not measured | Only GPT-6 Luna measured |
| Mystery game puzzles | 7.0% | Not measured | Only GPT-6 Luna measured |
| LMArena Text: hard prompts | 1410 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: expert | 1439 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: hard prompts in English | 1411 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: diagrams | 1245 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: homework | 1261 | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6 Luna | Mercury 2.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.9% | Not measured | Only GPT-6 Luna measured |
| FrontierMath, tiers 1 to 3 | 78.9% | Not measured | Only GPT-6 Luna measured |
| FrontierMath, tier 4 | 56.1% | Not measured | Only GPT-6 Luna measured |
| ProofBench | 64.0% | 3.0% | GPT-6 Luna |
| LMArena Text: math | 1410 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: mathematical work | 1428 | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6 Luna | Mercury 2.5 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 83.1% | Only Mercury 2.5 measured |
| MedCode (Vals AI) | 44.7% | 31.3% | GPT-6 Luna |
| SimpleQA Verified | 41.4% | Not measured | Only GPT-6 Luna measured |
| LMArena Text: science | 1386 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: medicine and healthcare | 1351 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: legal and government | 1396 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: business and finance | 1387 | Not measured | Only GPT-6 Luna measured |
| FACTS Benchmark Suite | 63.3% | Not measured | Only GPT-6 Luna measured |
| PRBench Legal | 42.9% | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-6 Luna | Mercury 2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1270 | Not measured | Only GPT-6 Luna measured |
| Design Arena: UI components | 1295 | Not measured | Only GPT-6 Luna measured |
| Design Arena: game dev | 1257 | Not measured | Only GPT-6 Luna measured |
| Design Arena: data viz | 1217 | Not measured | Only GPT-6 Luna measured |
| Design Arena: 3D | 1219 | Not measured | Only GPT-6 Luna measured |
| Design Arena: all code categories | 1270 | Not measured | Only GPT-6 Luna measured |
| Design Arena: SVG | 1253 | Not measured | Only GPT-6 Luna measured |
| Design Arena: ASCII art | 1153 | Not measured | Only GPT-6 Luna measured |
| LiveBench design | 82.5% | Not measured | Only GPT-6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-6 Luna | Mercury 2.5 | Leader |
|---|---|---|---|
| Vals Index | 52.3% | Not measured | Only GPT-6 Luna measured |
| LiveBench data analysis | 73.4% | Not measured | Only GPT-6 Luna measured |
| Public Benefits Bench (Vals AI) | 57.6% | 45.3% | GPT-6 Luna |
| SAGE (Vals AI) | 48.1% | Not measured | Only GPT-6 Luna measured |
| MedScribe (Vals AI) | 83.7% | 55.1% | GPT-6 Luna |
| Long context reasoning (AA-LCR) | 83.3% | 71.7% | GPT-6 Luna |
| DTBench | 90.1% | Not measured | Only GPT-6 Luna measured |
| LMCA | 44.5% | Not measured | Only GPT-6 Luna measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-6 Luna measured |
| LMArena Text | 1390 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: instruction following | 1407 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: creative writing | 1363 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision | 1216 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: English | 1394 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: non-English | 1381 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: Chinese | 1426 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: German | 1376 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: Japanese | 1369 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: Korean | 1364 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: French | 1406 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: Spanish | 1391 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: Russian | 1393 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: Polish | 1383 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: multi-turn | 1393 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: longer queries | 1407 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: writing and language | 1383 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: entertainment and media | 1363 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: English | 1204 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: Chinese | 1266 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: reading text | 1225 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: creative writing | 1205 | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6 Luna | Mercury 2.5 | 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-6 Luna$0.0003Fits in one request
Mercury 2.5$0.0001Fits in one request
Mercury 2.5 costs less
Repository review
50K input + 3K output tokens
GPT-6 Luna$0.0065Fits in one request
Mercury 2.5$0.0024Fits in one request
Mercury 2.5 costs less
Agent loop
200K input + 10K output tokens
GPT-6 Luna$0.025Fits in one request
Mercury 2.5$0.0095Fits in one request
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 | OpenAI GPT-6 Luna | Inception Mercury 2.5 |
|---|---|---|
| Maker | OpenAI | Inception |
| Weights | No public weights listed | No public weights listed |
| Released | September 22, 2026 | September 8, 2026 |
| Context window | 1.05M | 260K |
| Input $/Mtok | $0.10 | $0.04 |
| Output $/Mtok | $0.50 | $0.15 |
| Coding index | 57.3 | 1.6 |
| Independent measurements behind it | 5 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6 Luna better than Mercury 2.5 for coding?
Pick GPT-6 Luna for coding: it scores 57.3 on the Vibe Coding Index against 1.6 for Mercury 2.5. Pick Mercury 2.5 for output tokens 3.3x cheaper ($0.15 vs $0.50 per million).
Which is cheaper, GPT-6 Luna or Mercury 2.5?
GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens; Mercury 2.5 costs $0.04 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6 Luna or Mercury 2.5?
GPT-6 Luna, with 1.05M tokens against 260K.
How many benchmarks is the GPT-6 Luna vs Mercury 2.5 comparison based on?
20 benchmarks have a result for both models as of October 23, 2026. GPT-6 Luna's coding index rests on 5 independent benchmarks and Mercury 2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.