Model comparison
Grok 4.3 vs Mistral Large
Grok 4.3 is the stronger model overall, scoring 43.8 to 31.9 on the Noometry Index.
Last verified . 26 shared benchmarks.
Mistral AISummary
- They share 26 benchmarks with published results for both. Grok 4.3 scores higher in 8 categories and Mistral Large in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Grok 4.3 leads 46.0 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for Grok 4.3 and 8.5% for Mistral Large.
- Grok 4.3 is cheaper at $1.25 / $2.50 per million input/output tokens, against $2 / $6 for Mistral Large.
- Grok 4.3 accepts more context: 1M tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.
Side by side
| Grok 4.3 | Mistral Large | |
|---|---|---|
| Provider | xAI | Mistral AI |
| Noometry Index | 43.8 | 31.9 |
| Released | 2026-04-17 | 2024-02-26 |
| Weights | Proprietary | Open |
| Context window | 1M | 131K |
| Max output | 30K | 16K |
| Input $ / M tokens | $1.25 | $2 |
| Output $ / M tokens | $2.50 | $6 |
| Results tracked | 40 | 51 |
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Category by category
Coding Grok 4.3 leads
Grok 4.3: 41.6 (#121), Mistral Large: 34.3 (#240)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| SciCode | 47.3% | 36.2% |
| LMArena Coding | 1415 | 1277 |
| ALE-Bench | 944.17 | 264.7 |
| LMArena WebDev | 1357 | — |
| WeirdML | 49.9% | — |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use Too close to call
Grok 4.3: 27.7 (#99), Mistral Large: 28.6 (#89)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |
| GDP.pdf | 8% | — |
| LMArena Search | 1165 | — |
| Vending-Bench 2 | 35.26 | — |
Reasoning Grok 4.3 leads
Grok 4.3: 35.9 (#68), Mistral Large: 15.8 (#310)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| CritPt | 8% | 0% |
| LMArena Hard Prompts | 1396 | 1257 |
| DTBench | 90.7% | 65.1% |
| LMCA | 38.3% | 16.7% |
| Epoch Capabilities Index | 149.16 | 128.52 |
| ForecastBench | 60.3 | 57.1 |
| SimpleBench | — | 22.5% |
| NYT Connections (extended) | 55.2% | — |
| Chess Puzzles | 25% | — |
| LiveBench Reasoning | — | 43.5% |
| LiveBench Data Analysis | — | 50.1% |
| LiveBench | — | 48.4% |
Math Grok 4.3 leads
Grok 4.3: 46.0 (#74), Mistral Large: 18.2 (#291)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 8.5% |
| LMArena Math | 1388 | 1262 |
| FrontierMath (Tiers 1-3) | 42.8% | — |
| FrontierMath Tier 4 | 14.6% | — |
| ProofBench | 11% | — |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Grok 4.3 leads
Grok 4.3: 52.5 (#62), Mistral Large: 30.1 (#230)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| GPQA Diamond | 88.8% | 51.3% |
| LMArena Expert | 1385 | 1232 |
| SimpleQA Verified | 33.2% | — |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |
Multimodal Not comparable
Grok 4.3: 31.6 (#104), Mistral Large: —
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| LMArena Vision | 1229 | — |
| Blueprint-Bench 2 | 0% | — |
Multilingual Grok 4.3 leads
Grok 4.3: 50.5 (#120), Mistral Large: 40.0 (#219)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| LMArena Non-English | 1385 | 1237 |
| LMArena Chinese | 1422 | 1240 |
| LMArena French | 1412 | 1325 |
| LMArena German | 1395 | 1254 |
| LMArena Japanese | 1379 | 1188 |
| LMArena Korean | 1356 | 1202 |
| LMArena Russian | 1399 | 1257 |
| LMArena Spanish | 1398 | 1268 |
Instruction Following Grok 4.3 leads
Grok 4.3: 72.1 (#140), Mistral Large: 67.9 (#191)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1366 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context Grok 4.3 leads
Grok 4.3: 42.5 (#123), Mistral Large: 38.3 (#199)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1393 | 1261 |
Writing & Preference Grok 4.3 leads
Grok 4.3: 58.5 (#118), Mistral Large: 40.7 (#242)
| Benchmark | Grok 4.3 | Mistral Large |
|---|---|---|
| LMArena Text | 1397 | 1266 |
| LMArena Creative Writing | 1380 | 1243 |
| LMArena Multi-Turn | 1406 | 1260 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| EQ-Bench 4 | 1075 | — |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is Grok 4.3 better than Mistral Large?
Grok 4.3 is the stronger model overall, scoring 43.8 to 31.9 on the Noometry Index.
Which is cheaper, Grok 4.3 or Mistral Large?
Grok 4.3 is cheaper. It lists at $1.25 per million input tokens and $2.50 per million output tokens; Mistral Large lists at $2 and $6.
Is Grok 4.3 or Mistral Large better for coding?
Grok 4.3 scores higher on coding benchmarks: 41.6 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
Grok 4.3 does, with 1M tokens against 131K.
How many benchmarks do Grok 4.3 and Mistral Large share?
26 benchmarks have published results for both models. Grok 4.3 has 40 scored results on Noometry and Mistral Large has 51.