Mercury 2.5 vs Mistral Large 2407
Mercury 2.5 leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 12.3 to 7.6, and Mercury 2.5 costs 40.0x less per output token ($0.15 vs $6.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
Mercury 2.5 or Mistral Large 2407: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Mistral Large 2407 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 3
- Mercury 2.5 only
- 19
- Mistral Large 2407 only
- 14
- Like-for-like categories
- 3 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 7.6.
Same board, both measured
Long documents
Listed context window
Mercury 2.5
Mercury 2.5 takes 260K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Mercury 2.5 $0.0001, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- Mercury 2.5 1.6 from 2 benchmarks, Mistral Large 2407 not benchmarked.
- Widest category
- General, 69.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Mercury 2.5 71.7% against Mistral Large 2407 2.0%, 69.7 points apart on a 0 to 100 scale.
- Output price
- Mercury 2.5 $0.15 against Mistral Large 2407 $6.00 per million output tokens, 40.0x apart.
- Context window
- Mercury 2.5 takes 260K tokens, Mistral Large 2407 131K.
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 2407 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large 2407 | Leader |
|---|---|---|---|
| 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 | 26.7% | Only Mistral Large 2407 measured |
| SciCode | 38.5% | Not measured | Only Mercury 2.5 measured |
| ALE-Bench | 301.6 | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2.5 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.3 | 7.6 | Mercury 2.5 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large 2407 | 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 |
| τ²-Bench Telecom | Not measured | 33.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2.5 | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 47.2% | Only Mistral Large 2407 measured |
| Humanity's Last Exam (Artificial Analysis run) | 11.8% | 2.9% | Mercury 2.5 |
| GPQA Diamond (Epoch AI run) | Not measured | 49.0% | Only Mistral Large 2407 measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 0.0 | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 0.0% | Only Mistral Large 2407 measured |
| OTIS Mock AIME | Not measured | 8.5% | Only Mistral Large 2407 measured |
| ProofBench | 3.0% | Not measured | Only Mercury 2.5 measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large 2407 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.1% | Not measured | Only Mercury 2.5 measured |
| MedCode (Vals AI) | 31.3% | Not measured | Only Mercury 2.5 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 68.3% | Only Mistral Large 2407 measured |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2.5 | Mistral Large 2407 | 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 |
| IFBench | Not measured | 31.6% | Only Mistral Large 2407 measured |
| Long context reasoning (AA-LCR) | 71.7% | 2.0% | Mercury 2.5 |
| DTBench | Not measured | 61.2% | Only Mistral Large 2407 measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 40 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Mistral Large 2407 | 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 2407 |
|---|---|---|
| Maker | Inception | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | September 8, 2026 | November 19, 2024 |
| Context window | 260K | 131K |
| 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 2407 for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Mistral Large 2407 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 2407?
Mercury 2.5 costs $0.04 per million input tokens and $0.15 per million output tokens; Mistral Large 2407 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 2407?
Mercury 2.5, with 260K tokens against 131K.
How many benchmarks is the Mercury 2.5 vs Mistral Large 2407 comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. Mercury 2.5's coding index rests on 2 independent benchmarks and Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.