R1 0528 vs Mistral Large 4
Mistral Large 4 leads R1 0528 on the Artificial Analysis Intelligence Index, 38.4 to 13.1, and R1 0528 costs 1.9x less per output token ($2.18 vs $4.18 per million). Updated October 22, 2026; 38 benchmarks have a result for both.
R1 0528 or Mistral Large 4: which to pick
Only Mistral Large 4 has a coding score, 56.4 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding.
Pick R1 0528 for output tokens 1.9x cheaper ($2.18 vs $4.18 per million) and work measured by LMArena Text: medicine and healthcare or LMArena Text: creative writing, where it leads.
- Shared results
- 38
- R1 0528 only
- 22
- Mistral Large 4 only
- 43
- Like-for-like categories
- 7 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
Only Mistral Large 4 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Mistral Large 4
Mistral Large 4 leads, 38.4 against 13.1.
Same board, both measured
Web design
Design Arena: websites
Mistral Large 4
Mistral Large 4 leads, 1257 against 1149.
Same board, both measured
Long documents
Listed context window
Mistral Large 4
Mistral Large 4 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
R1 0528
R1 0528 $0.0016, Mistral Large 4 $0.0034 per turn.
List prices
At a glance
- Coding index
- R1 0528 not benchmarked, Mistral Large 4 56.4 from 5 benchmarks.
- Widest category
- Intelligence, 25.3 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: R1 0528 66.4% against Mistral Large 4 98.3%, 31.9 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.18 against Mistral Large 4 $4.18 per million output tokens, 1.9x apart.
- Context window
- R1 0528 takes 164K tokens, Mistral Large 4 1.05M.
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 | R1 0528 | Bars | Mistral Large 4 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 3 | ||
| Knowledge | 1 | 3 | ||
| Web design | 0 | 7 | ||
| General | 5 | 10 | ||
| All shared boards | 6 | 32 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | R1 0528 | Mistral Large 4 | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| LiveBench coding | Not measured | 60.0% | Only Mistral Large 4 measured |
| LMArena WebDev | Not measured | 1543 | Only Mistral Large 4 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 27.8% | Only Mistral Large 4 measured |
| Vibe Code Bench (Vals AI) | Not measured | 78.4% | Only Mistral Large 4 measured |
| Code migration (Vals AI) | Not measured | 30.6% | Only Mistral Large 4 measured |
| IOI (Vals AI) | Not measured | 45.3% | Only Mistral Large 4 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.1% | Only Mistral Large 4 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.3% | Only Mistral Large 4 measured |
| LiveCodeBench (Artificial Analysis run) | 77.0% | Not measured | Only R1 0528 measured |
| SciCode | Not measured | 54.2% | Only Mistral Large 4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | Not measured | Only R1 0528 measured |
| FrontierCode | Not measured | 39.4% | Only Mistral Large 4 measured |
| WeirdML | 41.6% | Not measured | Only R1 0528 measured |
| ALE-Bench | 804.1 | 1213.2 | Mistral Large 4 |
| LMArena Text: coding | 1427 | 1478 | Mistral Large 4 |
| LMArena Text: software and IT | 1435 | 1467 | Mistral Large 4 |
| LMArena WebDev: React | Not measured | 1559 | Only Mistral Large 4 measured |
| LMArena WebDev: HTML | Not measured | 1487 | Only Mistral Large 4 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | R1 0528 | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 38.4 | Mistral Large 4 |
| Epoch Capabilities Index | Not measured | 150.0 | Only Mistral Large 4 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | R1 0528 | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 28.1% | Only Mistral Large 4 measured |
| LiveBench browser | Not measured | 8.0% | Only Mistral Large 4 measured |
| LiveBench long agentic | Not measured | 11.8% | Only Mistral Large 4 measured |
| Finance Agent (Vals AI) | Not measured | 54.7% | Only Mistral Large 4 measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.4% | Only Mistral Large 4 measured |
| Legal Research Bench (Vals AI) | Not measured | 31.7% | Only Mistral Large 4 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 17.5% | Only Mistral Large 4 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.2% | Only Mistral Large 4 measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Mistral Large 4 measured |
| τ²-Bench Telecom | 36.5% | Not measured | Only R1 0528 measured |
| METR time horizons | 53.8% | Not measured | Only R1 0528 measured |
| DeepResearch Bench | 35.1% | Not measured | Only R1 0528 measured |
| BrowseComp-Plus | 16.4% | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | R1 0528 | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 83.9% | Only Mistral Large 4 measured |
| MysteryMechanism (Vals AI) | Not measured | 12.2% | Only Mistral Large 4 measured |
| GPQA Diamond (Artificial Analysis run) | 81.3% | Not measured | Only R1 0528 measured |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 35.0% | Mistral Large 4 |
| GPQA Diamond (Epoch AI run) | 76.3% | 88.1% | Mistral Large 4 |
| ARC-AGI | 21.2% | Not measured | Only R1 0528 measured |
| ARC-AGI-2 | 1.1% | Not measured | Only R1 0528 measured |
| SimpleBench | 40.8% | Not measured | Only R1 0528 measured |
| Chess puzzles | Not measured | 33.0% | Only Mistral Large 4 measured |
| Mystery game puzzles | Not measured | 17.0% | Only Mistral Large 4 measured |
| LMArena Text: hard prompts | 1415 | 1444 | Mistral Large 4 |
| LMArena Text: expert | 1394 | 1444 | Mistral Large 4 |
| LMArena Text: hard prompts in English | 1422 | 1442 | Mistral Large 4 |
| LMArena Vision: diagrams | Not measured | 1288 | Only Mistral Large 4 measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | R1 0528 | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Math Index | 76.0 | Not measured | Only R1 0528 measured |
| AIME 2025 (Artificial Analysis run) | 76.0% | Not measured | Only R1 0528 measured |
| OTIS Mock AIME | 66.4% | 98.3% | Mistral Large 4 |
| FrontierMath, tiers 1 to 3 | Not measured | 58.6% | Only Mistral Large 4 measured |
| FrontierMath, tier 4 | Not measured | 26.8% | Only Mistral Large 4 measured |
| ProofBench | Not measured | 10.0% | Only Mistral Large 4 measured |
| MATH level 5 | 96.6% | Not measured | Only R1 0528 measured |
| LMArena Text: math | 1400 | 1469 | Mistral Large 4 |
| LMArena Text: mathematical work | 1415 | 1462 | Mistral Large 4 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | R1 0528 | Mistral Large 4 | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 40.7% | Only Mistral Large 4 measured |
| MMLU Pro (Artificial Analysis run) | 84.9% | Not measured | Only R1 0528 measured |
| SimpleQA Verified | Not measured | 20.0% | Only Mistral Large 4 measured |
| LMArena Text: science | 1430 | 1448 | Mistral Large 4 |
| LMArena Text: medicine and healthcare | 1445 | 1433 | R1 0528 |
| LMArena Text: legal and government | 1419 | 1425 | Mistral Large 4 |
| LMArena Text: business and finance | 1402 | 1439 | Mistral Large 4 |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | R1 0528 | Mistral Large 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1149 | 1257 | Mistral Large 4 |
| Design Arena: UI components | 1100 | 1272 | Mistral Large 4 |
| Design Arena: game dev | 1097 | 1225 | Mistral Large 4 |
| Design Arena: data viz | 1182 | 1275 | Mistral Large 4 |
| Design Arena: 3D | 1110 | 1252 | Mistral Large 4 |
| Design Arena: all code categories | 1138 | 1259 | Mistral Large 4 |
| Design Arena: SVG | 1038 | 1232 | Mistral Large 4 |
| Design Arena: ASCII art | Not measured | 1202 | Only Mistral Large 4 measured |
| LiveBench design | Not measured | 65.6% | Only Mistral Large 4 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index510
| Benchmark | R1 0528 | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.5% | Only Mistral Large 4 measured |
| MedScribe (Vals AI) | Not measured | 80.4% | Only Mistral Large 4 measured |
| IFBench | 39.6% | Not measured | Only R1 0528 measured |
| Long context reasoning (AA-LCR) | 55.7% | 81.3% | Mistral Large 4 |
| DTBench | Not measured | 88.2% | Only Mistral Large 4 measured |
| LMCA | Not measured | 35.8% | Only Mistral Large 4 measured |
| Fiction.LiveBench | 75.0% | Not measured | Only R1 0528 measured |
| Creative writing (Lech Mazur) | 81.9% | Not measured | Only R1 0528 measured |
| Surface Evolver Bench | Not measured | 28.1% | Only Mistral Large 4 measured |
| Furniture assembly | Not measured | 20.8% | Only Mistral Large 4 measured |
| LMArena Text | 1428 | 1431 | Mistral Large 4 |
| LMArena Text: instruction following | 1382 | 1422 | Mistral Large 4 |
| LMArena Text: creative writing | 1405 | 1363 | R1 0528 |
| LMArena Vision | Not measured | 1272 | Only Mistral Large 4 measured |
| LMArena Text: English | 1437 | 1433 | R1 0528 |
| LMArena Text: non-English | 1412 | 1422 | Mistral Large 4 |
| LMArena Text: Chinese | 1442 | 1492 | Mistral Large 4 |
| LMArena Text: German | 1404 | Not measured | Only R1 0528 measured |
| LMArena Text: Japanese | 1391 | 1415 | Mistral Large 4 |
| LMArena Text: Korean | 1360 | Not measured | Only R1 0528 measured |
| LMArena Text: French | 1417 | 1492 | Mistral Large 4 |
| LMArena Text: Spanish | 1411 | 1455 | Mistral Large 4 |
| LMArena Text: Russian | 1423 | 1417 | R1 0528 |
| LMArena Text: Polish | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: multi-turn | 1405 | 1435 | Mistral Large 4 |
| LMArena Text: longer queries | 1391 | 1428 | Mistral Large 4 |
| LMArena Text: writing and language | 1402 | 1386 | R1 0528 |
| LMArena Text: entertainment and media | 1400 | 1378 | R1 0528 |
| LMArena Vision: English | Not measured | 1283 | Only Mistral Large 4 measured |
| LMArena Vision: reading text | Not measured | 1284 | Only Mistral Large 4 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | R1 0528 | Mistral Large 4 | 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
R1 0528 costs less
Repository review
50K input + 3K output tokens
R1 0528 costs less
Agent loop
200K input + 10K output tokens
R1 0528 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 | DeepSeek R1 0528 | Mistral Large 4 |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | May 28, 2025 | October 6, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.50 | $1.36 |
| Output $/Mtok | $2.18 | $4.18 |
| Coding index | Not benchmarked | 56.4 |
| Independent measurements behind it | 0 | 5 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is R1 0528 better than Mistral Large 4 for coding?
Only Mistral Large 4 has a coding score, 56.4 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 1.9x cheaper ($2.18 vs $4.18 per million) and work measured by LMArena Text: medicine and healthcare or LMArena Text: creative writing, where it leads.
Which is cheaper, R1 0528 or Mistral Large 4?
R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; Mistral Large 4 costs $1.36 and $4.18. Cost per solved task is known for neither of them.
Which has the bigger context window, R1 0528 or Mistral Large 4?
Mistral Large 4, with 1.05M tokens against 164K.
How many benchmarks is the R1 0528 vs Mistral Large 4 comparison based on?
38 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and Mistral Large 4's on 5. Where only one model has a result, the page says so instead of naming a winner.