Codestral 2508 vs Mercury 2.5
Mercury 2.5 costs 6.0x less per output token than Codestral 2508 ($0.15 vs $0.90 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
Codestral 2508 or Mercury 2.5: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 1
- Codestral 2508 only
- 7
- Mercury 2.5 only
- 21
- Like-for-like categories
- 1 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
No call
Only Mercury 2.5 has a AA Intelligence Index result, 12.3.
Directional only
Web design
Design Arena: websites
No call
Only Codestral 2508 has a Design Arena: websites result, 1013.
Directional only
Long documents
Listed context window
Mercury 2.5
Mercury 2.5 takes 260K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Codestral 2508 $0.0008, Mercury 2.5 $0.0001 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, Mercury 2.5 1.6 from 2 benchmarks.
- Widest category
- Coding, 5.5 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Codestral 2508 137.8 against Mercury 2.5 301.6, 5.5 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against Mercury 2.5 $0.15 per million output tokens, 6.0x apart.
- Context window
- Codestral 2508 takes 256K 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 | Codestral 2508 | Bars | Mercury 2.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Codestral 2508 | Mercury 2.5 | Leader |
|---|---|---|---|
| Terminal-Bench 4 (Vals AI) | Not measured | 0.0% | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 3.7% | Only Mercury 2.5 measured |
| Code migration (Vals AI) | Not measured | 4.5% | Only Mercury 2.5 measured |
| IOI (Vals AI) | Not measured | 2.4% | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 34.5% | Only Mercury 2.5 measured |
| SkillsBench (Vals AI) | Not measured | 18.1% | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | Not measured | 45.5% | Only Mercury 2.5 measured |
| SciCode | Not measured | 38.5% | Only Mercury 2.5 measured |
| ALE-Bench | 137.8 | 301.6 | Mercury 2.5 |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 12.3 | Only Mercury 2.5 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Codestral 2508 | Mercury 2.5 | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | Not measured | 18.9% | Only Mercury 2.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 2.5% | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 4.3% | Only Mercury 2.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 8.8% | Only Mercury 2.5 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Mercury 2.5 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | Mercury 2.5 | Leader |
|---|---|---|---|
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 11.8% | Only Mercury 2.5 measured |
| Kagi LLM Benchmark | 32.5% | Not measured | Only Codestral 2508 measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | Mercury 2.5 | Leader |
|---|---|---|---|
| ProofBench | Not measured | 3.0% | Only Mercury 2.5 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | Mercury 2.5 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 83.1% | Only Mercury 2.5 measured |
| MedCode (Vals AI) | Not measured | 31.3% | Only Mercury 2.5 measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Codestral 2508 | Mercury 2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | Not measured | Only Codestral 2508 measured |
| Design Arena: UI components | 1017 | Not measured | Only Codestral 2508 measured |
| Design Arena: game dev | 968 | Not measured | Only Codestral 2508 measured |
| Design Arena: data viz | 1017 | Not measured | Only Codestral 2508 measured |
| Design Arena: 3D | 1020 | Not measured | Only Codestral 2508 measured |
| Design Arena: all code categories | 1007 | Not measured | Only Codestral 2508 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Codestral 2508 | Mercury 2.5 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 45.3% | Only Mercury 2.5 measured |
| MedScribe (Vals AI) | Not measured | 55.1% | Only Mercury 2.5 measured |
| Long context reasoning (AA-LCR) | Not measured | 71.7% | Only Mercury 2.5 measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | 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
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 | Mistral Codestral 2508 | Inception Mercury 2.5 |
|---|---|---|
| Maker | Mistral AI | Inception |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | September 8, 2026 |
| Context window | 256K | 260K |
| Input $/Mtok | $0.30 | $0.04 |
| Output $/Mtok | $0.90 | $0.15 |
| Coding index | Not benchmarked | 1.6 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than Mercury 2.5 for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Codestral 2508 or Mercury 2.5?
Codestral 2508 costs $0.30 per million input tokens and $0.90 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, Codestral 2508 or Mercury 2.5?
Mercury 2.5, with 260K tokens against 256K.
How many benchmarks is the Codestral 2508 vs Mercury 2.5 comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 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.