Grok Build 0.1 vs Mistral Large
Grok Build 0.1 leads Mistral Large on the Artificial Analysis Intelligence Index, 27.2 to 5.8, and Grok Build 0.1 costs 3.0x less per output token ($2.00 vs $6.00 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
Grok Build 0.1 or Mistral Large: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Grok Build 0.1 for output tokens 3.0x cheaper ($2.00 vs $6.00 per million) and a larger context window (256K against 128K tokens).
- Shared results
- 1
- Grok Build 0.1 only
- 2
- Mistral Large only
- 43
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Grok Build 0.1
Grok Build 0.1 leads, 27.2 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Large has a GPQA Diamond result, 47.7%.
Directional only
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Large has a MMLU Pro result, 69.7%.
Directional only
Long documents
Listed context window
Grok Build 0.1
Grok Build 0.1 takes 256K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Grok Build 0.1
Grok Build 0.1 $0.0020, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- Grok Build 0.1 not benchmarked, Mistral Large not benchmarked.
- Widest category
- Intelligence, 21.4 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: Grok Build 0.1 27.2 against Mistral Large 5.8, 21.4 points apart on a 0 to 100 scale.
- Output price
- Grok Build 0.1 $2.00 against Mistral Large $6.00 per million output tokens, 3.0x apart.
- Context window
- Grok Build 0.1 takes 256K tokens, Mistral Large 128K.
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 | Grok Build 0.1 | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards, none shared
| Benchmark | Grok Build 0.1 | Mistral Large | Leader |
|---|---|---|---|
| AA Coding Index | 51.5 | Not measured | Only Grok Build 0.1 measured |
| LiveCodeBench (Vals AI) | Not measured | 37.1% | Only Mistral Large measured |
| Vibe Code Bench (Vals AI) | 13.4% | Not measured | Only Grok Build 0.1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 17.8% | Only Mistral Large measured |
| LMArena Text: coding | Not measured | 1275 | Only Mistral Large measured |
| LMArena Text: software and IT | Not measured | 1274 | Only Mistral Large measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Grok Build 0.1 | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.2 | 5.8 | Grok Build 0.1 |
| Epoch Capabilities Index | Not measured | 122.1 | Only Mistral Large measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Grok Build 0.1 | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Grok Build 0.1 | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 47.7% | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 35.1% | Only Mistral Large measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.5% | Only Mistral Large measured |
| LMArena Text: hard prompts | Not measured | 1257 | Only Mistral Large measured |
| LMArena Text: expert | Not measured | 1208 | Only Mistral Large measured |
| LMArena Text: hard prompts in English | Not measured | 1268 | Only Mistral Large measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Grok Build 0.1 | Mistral Large | Leader |
|---|---|---|---|
| AIME | Not measured | 9.2% | Only Mistral Large measured |
| MATH 500 (Vals AI) | Not measured | 74.4% | Only Mistral Large measured |
| MGSM (Vals AI) | Not measured | 87.2% | Only Mistral Large measured |
| LMArena Text: math | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: mathematical work | Not measured | 1257 | Only Mistral Large measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Grok Build 0.1 | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 69.7% | Only Mistral Large measured |
| TaxEval (Vals AI) | Not measured | 63.8% | Only Mistral Large measured |
| MedQA (Vals AI) | Not measured | 76.2% | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 51.5% | Only Mistral Large measured |
| MMLU | Not measured | 68.8% | Only Mistral Large measured |
| LMArena Text: science | Not measured | 1260 | Only Mistral Large measured |
| LMArena Text: medicine and healthcare | Not measured | 1255 | Only Mistral Large measured |
| LMArena Text: legal and government | Not measured | 1275 | Only Mistral Large measured |
| LMArena Text: business and finance | Not measured | 1238 | Only Mistral Large measured |
| Neither model has a result on the other 9 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Grok Build 0.1 | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | Grok Build 0.1 | Mistral Large | Leader |
|---|---|---|---|
| DTBench | Not measured | 60.8% | Only Mistral Large measured |
| LMArena Text | Not measured | 1265 | Only Mistral Large measured |
| LMArena Text: instruction following | Not measured | 1249 | Only Mistral Large measured |
| LMArena Text: creative writing | Not measured | 1242 | Only Mistral Large measured |
| LMArena Text: English | Not measured | 1284 | Only Mistral Large measured |
| LMArena Text: non-English | Not measured | 1235 | Only Mistral Large measured |
| LMArena Text: Chinese | Not measured | 1239 | Only Mistral Large measured |
| LMArena Text: German | Not measured | 1242 | Only Mistral Large measured |
| LMArena Text: Japanese | Not measured | 1170 | Only Mistral Large measured |
| LMArena Text: Korean | Not measured | 1202 | Only Mistral Large measured |
| LMArena Text: French | Not measured | 1325 | Only Mistral Large measured |
| LMArena Text: Spanish | Not measured | 1268 | Only Mistral Large measured |
| LMArena Text: Russian | Not measured | 1252 | Only Mistral Large measured |
| LMArena Text: Polish | Not measured | 1157 | Only Mistral Large measured |
| LMArena Text: multi-turn | Not measured | 1260 | Only Mistral Large measured |
| LMArena Text: longer queries | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: writing and language | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: entertainment and media | Not measured | 1230 | Only Mistral Large measured |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Grok Build 0.1 | Mistral Large | 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
Grok Build 0.1 costs less
Repository review
50K input + 3K output tokens
Grok Build 0.1 costs less
Agent loop
200K input + 10K output tokens
Grok Build 0.1 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 | SpaceXAI Grok Build 0.1 | Mistral Large |
|---|---|---|
| Maker | xAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | May 20, 2026 | February 26, 2024 |
| Context window | 256K | 128K |
| Input $/Mtok | $1.00 | $2.00 |
| Output $/Mtok | $2.00 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Grok Build 0.1 better than Mistral Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Grok Build 0.1 for output tokens 3.0x cheaper ($2.00 vs $6.00 per million) and a larger context window (256K against 128K tokens).
Which is cheaper, Grok Build 0.1 or Mistral Large?
Grok Build 0.1 costs $1.00 per million input tokens and $2.00 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Grok Build 0.1 or Mistral Large?
Grok Build 0.1, with 256K tokens against 128K.
How many benchmarks is the Grok Build 0.1 vs Mistral Large comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. Grok Build 0.1's coding index rests on 1 independent benchmark and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.