Codestral 2508 vs GLM 5 Turbo
Updated October 21, 2026. 7 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Codestral 2508 has the edge where output tokens cost 4.4x less, and where the context window is larger, 256K against 203K tokens.
- Shared results
- 7
- Codestral 2508 only
- 0
- GLM 5 Turbo only
- 9
- Like-for-like categories
- 2 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 5 Turbo has a AA Intelligence Index result, 26.6.
Directional only
Web design
Design Arena: websites
GLM 5 Turbo
GLM 5 Turbo leads, 1262 against 1013.
Same board, both measured
Long documents
Listed context window
Codestral 2508
Codestral 2508 takes 256K tokens against 203K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, GLM 5 Turbo $0.0032 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, GLM 5 Turbo not benchmarked.
- Widest category
- Web design, 31.3 points apart on average over 6 shared benchmarks.
- Widest single gap
- Design Arena: game dev: Codestral 2508 968 against GLM 5 Turbo 1248, 35.0 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GLM 5 Turbo $4.00 per million output tokens, 4.4x apart.
- Context window
- Codestral 2508 takes 256K tokens, GLM 5 Turbo 203K.
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 | GLM 5 Turbo | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Web design | 0 | 6 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Codestral 2508 | GLM 5 Turbo | Leader |
|---|---|---|---|
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only GLM 5 Turbo measured |
| ALE-Bench | 137.8 | 634.0 | GLM 5 Turbo |
| Neither model has a result on the other 36 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | GLM 5 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 26.6 | Only GLM 5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | GLM 5 Turbo | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 98.5% | Only GLM 5 Turbo measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GLM 5 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.7% | Only GLM 5 Turbo measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 27.8% | Only GLM 5 Turbo measured |
| Neither model has a result on the other 19 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GLM 5 Turbo | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GLM 5 Turbo | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Codestral 2508 | GLM 5 Turbo | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 1262 | GLM 5 Turbo |
| Design Arena: UI components | 1017 | 1263 | GLM 5 Turbo |
| Design Arena: game dev | 968 | 1248 | GLM 5 Turbo |
| Design Arena: data viz | 1017 | 1263 | GLM 5 Turbo |
| Design Arena: 3D | 1020 | 1248 | GLM 5 Turbo |
| Design Arena: all code categories | 1007 | 1262 | GLM 5 Turbo |
| Design Arena: SVG | Not measured | 1206 | Only GLM 5 Turbo measured |
| Design Arena: ASCII art | Not measured | 1160 | Only GLM 5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GLM 5 Turbo | Leader |
|---|---|---|---|
| IFBench | Not measured | 73.2% | Only GLM 5 Turbo measured |
| Long context reasoning (AA-LCR) | Not measured | 71.7% | Only GLM 5 Turbo measured |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GLM 5 Turbo | 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
Codestral 2508 costs less
Repository review
50K input + 3K output tokens
Codestral 2508 costs less
Agent loop
200K input + 10K output tokens
Codestral 2508 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 | Z.ai GLM 5 Turbo |
|---|---|---|
| Maker | Mistral AI | Z.ai |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | March 15, 2026 |
| Context window | 256K | 203K |
| Input $/Mtok | $0.30 | $1.20 |
| Output $/Mtok | $0.90 | $4.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than GLM 5 Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Codestral 2508 has the edge where output tokens cost 4.4x less, and where the context window is larger, 256K against 203K tokens.
Which is cheaper, Codestral 2508 or GLM 5 Turbo?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GLM 5 Turbo costs $1.20 and $4.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or GLM 5 Turbo?
Codestral 2508, with 256K tokens against 203K.
How many benchmarks is the Codestral 2508 vs GLM 5 Turbo comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 independent benchmarks and GLM 5 Turbo's on 0. Where only one model has a result, the page says so instead of naming a winner.