Mercury 2.5 vs Mistral Large
Mercury 2.5 leads Mistral Large on the Artificial Analysis Intelligence Index, 12.3 to 5.8, and Mercury 2.5 costs 40.0x less per output token ($0.15 vs $6.00 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
Mercury 2.5 or Mistral Large: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 2
- Mercury 2.5 only
- 20
- Mistral Large only
- 42
- Like-for-like categories
- 2 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 Mercury 2.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Mercury 2.5
Mercury 2.5 leads, 12.3 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Large has a GPQA Diamond result, 47.7%.
Directional only
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Large has a MMLU Pro result, 69.7%.
Directional only
Long documents
Listed context window
Mercury 2.5
Mercury 2.5 takes 260K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Mercury 2.5 $0.0001, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- Mercury 2.5 1.6 from 2 benchmarks, Mistral Large not benchmarked.
- Widest category
- Reasoning, 8.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): Mercury 2.5 11.8% against Mistral Large 3.5%, 8.3 points apart on a 0 to 100 scale.
- Output price
- Mercury 2.5 $0.15 against Mistral Large $6.00 per million output tokens, 40.0x apart.
- Context window
- Mercury 2.5 takes 260K tokens, Mistral Large 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 | Mercury 2.5 | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | Not measured | 37.1% | Only Mistral Large measured |
| Terminal-Bench 4 (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | 3.7% | Not measured | Only Mercury 2.5 measured |
| Code migration (Vals AI) | 4.5% | Not measured | Only Mercury 2.5 measured |
| IOI (Vals AI) | 2.4% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 34.5% | Not measured | Only Mercury 2.5 measured |
| SkillsBench (Vals AI) | 18.1% | Not measured | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | 45.5% | Not measured | Only Mercury 2.5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 17.8% | Only Mistral Large measured |
| SciCode | 38.5% | Not measured | Only Mercury 2.5 measured |
| ALE-Bench | 301.6 | Not measured | Only Mercury 2.5 measured |
| LMArena Text: coding | Not measured | 1275 | Only Mistral Large measured |
| LMArena Text: software and IT | Not measured | 1274 | Only Mistral Large measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2.5 | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.3 | 5.8 | Mercury 2.5 |
| Epoch Capabilities Index | Not measured | 122.1 | Only Mistral Large measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | 18.9% | Not measured | Only Mercury 2.5 measured |
| Tax Agent Bench (Vals AI) | 2.5% | Not measured | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | 4.3% | Not measured | Only Mercury 2.5 measured |
| Excel Modeling Benchmark (Vals AI) | 8.8% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2.5 | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 47.7% | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 35.1% | Only Mistral Large measured |
| Humanity's Last Exam (Artificial Analysis run) | 11.8% | 3.5% | Mercury 2.5 |
| LMArena Text: hard prompts | Not measured | 1257 | Only Mistral Large measured |
| LMArena Text: expert | Not measured | 1208 | Only Mistral Large measured |
| LMArena Text: hard prompts in English | Not measured | 1268 | Only Mistral Large measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large | Leader |
|---|---|---|---|
| AIME | Not measured | 9.2% | Only Mistral Large measured |
| MATH 500 (Vals AI) | Not measured | 74.4% | Only Mistral Large measured |
| MGSM (Vals AI) | Not measured | 87.2% | Only Mistral Large measured |
| ProofBench | 3.0% | Not measured | Only Mercury 2.5 measured |
| LMArena Text: math | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: mathematical work | Not measured | 1257 | Only Mistral Large measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 69.7% | Only Mistral Large measured |
| LegalBench (Vals AI) | 83.1% | Not measured | Only Mercury 2.5 measured |
| TaxEval (Vals AI) | Not measured | 63.8% | Only Mistral Large measured |
| MedQA (Vals AI) | Not measured | 76.2% | Only Mistral Large measured |
| MedCode (Vals AI) | 31.3% | Not measured | Only Mercury 2.5 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 51.5% | Only Mistral Large measured |
| MMLU | Not measured | 68.8% | Only Mistral Large measured |
| LMArena Text: science | Not measured | 1260 | Only Mistral Large measured |
| LMArena Text: medicine and healthcare | Not measured | 1255 | Only Mistral Large measured |
| LMArena Text: legal and government | Not measured | 1275 | Only Mistral Large measured |
| LMArena Text: business and finance | Not measured | 1238 | Only Mistral Large measured |
| Neither model has a result on the other 7 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 45.3% | Not measured | Only Mercury 2.5 measured |
| MedScribe (Vals AI) | 55.1% | Not measured | Only Mercury 2.5 measured |
| Long context reasoning (AA-LCR) | 71.7% | Not measured | Only Mercury 2.5 measured |
| DTBench | Not measured | 60.8% | Only Mistral Large measured |
| LMArena Text | Not measured | 1265 | Only Mistral Large measured |
| LMArena Text: instruction following | Not measured | 1249 | Only Mistral Large measured |
| LMArena Text: creative writing | Not measured | 1242 | Only Mistral Large measured |
| LMArena Text: English | Not measured | 1284 | Only Mistral Large measured |
| LMArena Text: non-English | Not measured | 1235 | Only Mistral Large measured |
| LMArena Text: Chinese | Not measured | 1239 | Only Mistral Large measured |
| LMArena Text: German | Not measured | 1242 | Only Mistral Large measured |
| LMArena Text: Japanese | Not measured | 1170 | Only Mistral Large measured |
| LMArena Text: Korean | Not measured | 1202 | Only Mistral Large measured |
| LMArena Text: French | Not measured | 1325 | Only Mistral Large measured |
| LMArena Text: Spanish | Not measured | 1268 | Only Mistral Large measured |
| LMArena Text: Russian | Not measured | 1252 | Only Mistral Large measured |
| LMArena Text: Polish | Not measured | 1157 | Only Mistral Large measured |
| LMArena Text: multi-turn | Not measured | 1260 | Only Mistral Large measured |
| LMArena Text: longer queries | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: writing and language | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: entertainment and media | Not measured | 1230 | Only Mistral Large measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large | 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 2.5 costs less
Repository review
50K input + 3K output tokens
Mercury 2.5 costs less
Agent loop
200K input + 10K output tokens
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 | Inception Mercury 2.5 | Mistral Large |
|---|---|---|
| Maker | Inception | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | September 8, 2026 | February 26, 2024 |
| Context window | 260K | 128K |
| Input $/Mtok | $0.04 | $2.00 |
| Output $/Mtok | $0.15 | $6.00 |
| Coding index | 1.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2.5 better than Mistral Large for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Mercury 2.5 or Mistral Large?
Mercury 2.5 costs $0.04 per million input tokens and $0.15 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 2.5 or Mistral Large?
Mercury 2.5, with 260K tokens against 128K.
How many benchmarks is the Mercury 2.5 vs Mistral Large comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. Mercury 2.5's coding index rests on 2 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.