R1 0528 vs Mistral Large
R1 0528 leads Mistral Large on the Artificial Analysis Intelligence Index, 13.1 to 5.8, and R1 0528 costs 2.8x less per output token ($2.18 vs $6.00 per million). Updated October 21, 2026; 33 benchmarks have a result for both.
R1 0528 or Mistral Large: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick R1 0528 for output tokens 2.8x cheaper ($2.18 vs $6.00 per million) and a larger context window (164K against 128K tokens).
- Shared results
- 33
- R1 0528 only
- 26
- Mistral Large only
- 11
- Like-for-like categories
- 6 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
R1 0528
R1 0528 leads, 13.1 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
Web design
Design Arena: websites
No call
Only R1 0528 has a Design Arena: websites result, 1149.
Directional only
Long documents
Listed context window
R1 0528
R1 0528 takes 164K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
R1 0528
R1 0528 $0.0016, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- R1 0528 not benchmarked, Mistral Large not benchmarked.
- Widest category
- Coding, 37.1 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveCodeBench (Artificial Analysis run): R1 0528 77.0% against Mistral Large 17.8%, 59.2 points apart on a 0 to 100 scale.
- Output price
- R1 0528 $2.18 against Mistral Large $6.00 per million output tokens, 2.8x apart.
- Context window
- R1 0528 takes 164K 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 | R1 0528 | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 33 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | R1 0528 | Mistral Large | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| LiveCodeBench (Vals AI) | Not measured | 37.1% | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 77.0% | 17.8% | R1 0528 |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | Not measured | Only R1 0528 measured |
| WeirdML | 41.6% | Not measured | Only R1 0528 measured |
| ALE-Bench | 804.1 | Not measured | Only R1 0528 measured |
| LMArena Text: coding | 1427 | 1275 | R1 0528 |
| LMArena Text: software and IT | 1435 | 1274 | R1 0528 |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | R1 0528 | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 5.8 | R1 0528 |
| Epoch Capabilities Index | Not measured | 122.1 | Only Mistral Large measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | R1 0528 | Mistral Large | Leader |
|---|---|---|---|
| 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 |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | R1 0528 | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 47.7% | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 81.3% | 35.1% | R1 0528 |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | 3.5% | R1 0528 |
| GPQA Diamond (Epoch AI run) | 76.3% | Not measured | Only R1 0528 measured |
| 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 |
| LMArena Text: hard prompts | 1415 | 1257 | R1 0528 |
| LMArena Text: expert | 1394 | 1208 | R1 0528 |
| LMArena Text: hard prompts in English | 1422 | 1268 | R1 0528 |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | R1 0528 | 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 |
| 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% | Not measured | Only R1 0528 measured |
| MATH level 5 | 96.6% | Not measured | Only R1 0528 measured |
| LMArena Text: math | 1400 | 1261 | R1 0528 |
| LMArena Text: mathematical work | 1415 | 1257 | R1 0528 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | R1 0528 | 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) | 84.9% | 51.5% | R1 0528 |
| MMLU | Not measured | 68.8% | Only Mistral Large measured |
| LMArena Text: science | 1430 | 1260 | R1 0528 |
| LMArena Text: medicine and healthcare | 1445 | 1255 | R1 0528 |
| LMArena Text: legal and government | 1419 | 1275 | R1 0528 |
| LMArena Text: business and finance | 1402 | 1238 | R1 0528 |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | R1 0528 | Mistral Large | Leader |
|---|---|---|---|
| Design Arena: websites | 1149 | Not measured | Only R1 0528 measured |
| Design Arena: UI components | 1103 | Not measured | Only R1 0528 measured |
| Design Arena: game dev | 1097 | Not measured | Only R1 0528 measured |
| Design Arena: data viz | 1182 | Not measured | Only R1 0528 measured |
| Design Arena: 3D | 1111 | Not measured | Only R1 0528 measured |
| Design Arena: all code categories | 1139 | Not measured | Only R1 0528 measured |
| Design Arena: SVG | 1039 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
| Benchmark | R1 0528 | Mistral Large | Leader |
|---|---|---|---|
| IFBench | 39.6% | Not measured | Only R1 0528 measured |
| Long context reasoning (AA-LCR) | 55.7% | Not measured | Only R1 0528 measured |
| DTBench | Not measured | 60.8% | Only Mistral Large measured |
| Fiction.LiveBench | 75.0% | Not measured | Only R1 0528 measured |
| Creative writing (Lech Mazur) | 81.9% | Not measured | Only R1 0528 measured |
| LMArena Text | 1428 | 1265 | R1 0528 |
| LMArena Text: instruction following | 1382 | 1249 | R1 0528 |
| LMArena Text: creative writing | 1405 | 1242 | R1 0528 |
| LMArena Text: English | 1437 | 1284 | R1 0528 |
| LMArena Text: non-English | 1412 | 1235 | R1 0528 |
| LMArena Text: Chinese | 1442 | 1239 | R1 0528 |
| LMArena Text: German | 1404 | 1242 | R1 0528 |
| LMArena Text: Japanese | 1391 | 1170 | R1 0528 |
| LMArena Text: Korean | 1360 | 1202 | R1 0528 |
| LMArena Text: French | 1417 | 1325 | R1 0528 |
| LMArena Text: Spanish | 1411 | 1268 | R1 0528 |
| LMArena Text: Russian | 1423 | 1252 | R1 0528 |
| LMArena Text: Polish | 1428 | 1157 | R1 0528 |
| LMArena Text: multi-turn | 1405 | 1260 | R1 0528 |
| LMArena Text: longer queries | 1391 | 1261 | R1 0528 |
| LMArena Text: writing and language | 1402 | 1261 | R1 0528 |
| LMArena Text: entertainment and media | 1400 | 1230 | R1 0528 |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | R1 0528 | 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
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 |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | May 28, 2025 | February 26, 2024 |
| Context window | 164K | 128K |
| Input $/Mtok | $0.50 | $2.00 |
| Output $/Mtok | $2.18 | $6.00 |
| 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 R1 0528 better than Mistral Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 2.8x cheaper ($2.18 vs $6.00 per million) and a larger context window (164K against 128K tokens).
Which is cheaper, R1 0528 or Mistral Large?
R1 0528 costs $0.50 per million input tokens and $2.18 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, R1 0528 or Mistral Large?
R1 0528, with 164K tokens against 128K.
How many benchmarks is the R1 0528 vs Mistral Large comparison based on?
33 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 0 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.