Codestral 2508 vs Llama 3.2 3B Instruct
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.90
- Coding
- No data
- Overall
- 5.7
- Output $/M
- $0.3325
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Codestral 2508 has the edge where the context window is larger, 256K against 131K tokens.
- Shared results
- 0
- Codestral 2508 only
- 7
- Llama 3.2 3B Instruct only
- 34
- Like-for-like categories
- 0 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 Llama 3.2 3B Instruct has a AA Intelligence Index result, 5.7.
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
Codestral 2508
Codestral 2508 takes 256K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 3B Instruct
Codestral 2508 $0.0008, Llama 3.2 3B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, Llama 3.2 3B Instruct not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- Codestral 2508 $0.90 against Llama 3.2 3B Instruct $0.3325 per million output tokens, 2.7x apart.
- Context window
- Codestral 2508 takes 256K tokens, Llama 3.2 3B Instruct 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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 8.3% | Only Llama 3.2 3B Instruct measured |
| ALE-Bench | 137.8 | Not measured | Only Codestral 2508 measured |
| LMArena Text: coding | Not measured | 1098 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: software and IT | Not measured | 1103 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 5.7 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 21.1% | Only Llama 3.2 3B Instruct measured |
| BALROG | Not measured | 10.1% | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 25.5% | Only Llama 3.2 3B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 5.3% | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts | Not measured | 1095 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: expert | Not measured | 1090 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1141 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 3.3 | Only Llama 3.2 3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 3.3% | Only Llama 3.2 3B Instruct measured |
| LMArena Text: math | Not measured | 1126 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1128 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 34.7% | Only Llama 3.2 3B Instruct measured |
| LMArena Text: science | Not measured | 1106 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1104 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: legal and government | Not measured | 1115 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: business and finance | Not measured | 1074 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 26.2% | Only Llama 3.2 3B Instruct measured |
| Long context reasoning (AA-LCR) | Not measured | 4.3% | Only Llama 3.2 3B Instruct measured |
| LMArena Text | Not measured | 1110 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: instruction following | Not measured | 1089 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: creative writing | Not measured | 1094 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: English | Not measured | 1180 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: non-English | Not measured | 1019 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Chinese | Not measured | 1017 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: German | Not measured | 1056 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: Russian | Not measured | 949 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1105 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: longer queries | Not measured | 1100 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: writing and language | Not measured | 1096 | Only Llama 3.2 3B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1083 | Only Llama 3.2 3B Instruct measured |
| Neither model has a result on the other 32 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | Llama 3.2 3B Instruct | 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
Llama 3.2 3B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 3B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 3B Instruct 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 | Meta Llama 3.2 3B Instruct |
|---|---|---|
| Maker | Mistral AI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | August 1, 2025 | September 25, 2024 |
| Context window | 256K | 131K |
| Input $/Mtok | $0.30 | $0.0505 |
| Output $/Mtok | $0.90 | $0.3325 |
| 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 Llama 3.2 3B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Codestral 2508 has the edge where the context window is larger, 256K against 131K tokens.
Which is cheaper, Codestral 2508 or Llama 3.2 3B Instruct?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; Llama 3.2 3B Instruct costs $0.0505 and $0.3325. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or Llama 3.2 3B Instruct?
Codestral 2508, with 256K tokens against 131K.
How many benchmarks is the Codestral 2508 vs Llama 3.2 3B Instruct comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 independent benchmarks and Llama 3.2 3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.