Codestral 2508 vs GPT-3.5 Turbo Instruct
Codestral 2508 costs 2.2x less per output token than GPT-3.5 Turbo Instruct ($0.90 vs $2.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.90
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
Codestral 2508 or GPT-3.5 Turbo Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Codestral 2508 for output tokens 2.2x cheaper ($0.90 vs $2.00 per million) and a larger context window (256K against 4K tokens).
No benchmark has tested both yet. Codestral 2508 has results on ALE-Bench, Kagi LLM Benchmark and Design Arena: websites; GPT-3.5 Turbo Instruct on METR time horizons. Compare them on price and context instead.
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
Web design
Design Arena: websites
No call
Only Codestral 2508 has a Design Arena: websites result, 1013.
Directional only
Long documents
Listed context window
Codestral 2508
Codestral 2508 takes 256K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, GPT-3.5 Turbo Instruct $0.0025 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, GPT-3.5 Turbo Instruct not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Codestral 2508 $0.90 against GPT-3.5 Turbo Instruct $2.00 per million output tokens, 2.2x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-3.5 Turbo Instruct 4K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board measured
| Benchmark | Codestral 2508 | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| ALE-Bench | 137.8 | Not measured | Only Codestral 2508 measured |
| Neither model has a result on the other 38 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Codestral 2508 | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| METR time horizons | Not measured | 21.5% | Only GPT-3.5 Turbo Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Codestral 2508 | GPT-3.5 Turbo Instruct | Leader |
|---|---|---|---|
| Kagi LLM Benchmark | 32.5% | Not measured | Only Codestral 2508 measured |
| Neither model has a result on the other 21 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Codestral 2508 | GPT-3.5 Turbo Instruct | 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 | |||
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 | OpenAI GPT-3.5 Turbo Instruct |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | September 28, 2023 |
| Context window | 256K | 4K |
| Input $/Mtok | $0.30 | $1.50 |
| Output $/Mtok | $0.90 | $2.00 |
| 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 GPT-3.5 Turbo Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Codestral 2508 for output tokens 2.2x cheaper ($0.90 vs $2.00 per million) and a larger context window (256K against 4K tokens).
Which is cheaper, Codestral 2508 or GPT-3.5 Turbo Instruct?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-3.5 Turbo Instruct costs $1.50 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or GPT-3.5 Turbo Instruct?
Codestral 2508, with 256K tokens against 4K.
Has any benchmark tested both Codestral 2508 and GPT-3.5 Turbo Instruct?
No benchmark has tested both yet. Codestral 2508 has results on ALE-Bench, Kagi LLM Benchmark and Design Arena: websites; GPT-3.5 Turbo Instruct on METR time horizons. Compare them on price and context instead. Data as of October 21, 2026.