R1 0528 vs Mistral Nemo
Mistral Nemo costs 14.5x less per output token than R1 0528 ($0.15 vs $2.18 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 46.8
- Output $/M
- $2.18
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
R1 0528 or Mistral Nemo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick R1 0528 for a larger context window (164K against 131K tokens).
No benchmark has tested both yet. R1 0528 has results on Aider polyglot, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Mistral Nemo on Epoch Capabilities Index, MMLU and DTBench. Compare them on price and context instead.
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 R1 0528 has a AA Intelligence Index result, 13.1.
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 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Nemo
R1 0528 $0.0016, Mistral Nemo $0.0002 per turn.
List prices
At a glance
- Coding index
- R1 0528 not benchmarked, Mistral Nemo not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- R1 0528 $2.18 against Mistral Nemo $0.15 per million output tokens, 14.5x apart.
- Context window
- R1 0528 takes 164K tokens, Mistral Nemo 131K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards measured
| Benchmark | R1 0528 | Mistral Nemo | Leader |
|---|---|---|---|
| Aider polyglot | 71.4% | Not measured | Only R1 0528 measured |
| LiveCodeBench (Artificial Analysis run) | 77.0% | Not measured | Only R1 0528 measured |
| 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 | Not measured | Only R1 0528 measured |
| LMArena Text: software and IT | 1435 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | R1 0528 | Mistral Nemo | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | Not measured | Only R1 0528 measured |
| Epoch Capabilities Index | Not measured | 118.7 | Only Mistral Nemo measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | R1 0528 | Mistral Nemo | 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 |
| BrowseComp-Plus | 16.4% | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | R1 0528 | Mistral Nemo | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | Not measured | Only R1 0528 measured |
| Humanity's Last Exam (Artificial Analysis run) | 15.8% | Not measured | Only R1 0528 measured |
| 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 | Not measured | Only R1 0528 measured |
| LMArena Text: expert | 1394 | Not measured | Only R1 0528 measured |
| LMArena Text: hard prompts in English | 1422 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | R1 0528 | Mistral Nemo | 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% | Not measured | Only R1 0528 measured |
| MATH level 5 | 96.6% | Not measured | Only R1 0528 measured |
| LMArena Text: math | 1400 | Not measured | Only R1 0528 measured |
| LMArena Text: mathematical work | 1415 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | R1 0528 | Mistral Nemo | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 84.9% | Not measured | Only R1 0528 measured |
| MMLU | Not measured | 65.3% | Only Mistral Nemo measured |
| LMArena Text: science | 1430 | Not measured | Only R1 0528 measured |
| LMArena Text: medicine and healthcare | 1445 | Not measured | Only R1 0528 measured |
| LMArena Text: legal and government | 1419 | Not measured | Only R1 0528 measured |
| LMArena Text: business and finance | 1402 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | R1 0528 | Mistral Nemo | 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards measured
| Benchmark | R1 0528 | Mistral Nemo | 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 | 48.6% | Only Mistral Nemo 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 | Not measured | Only R1 0528 measured |
| LMArena Text: instruction following | 1382 | Not measured | Only R1 0528 measured |
| LMArena Text: creative writing | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: English | 1437 | Not measured | Only R1 0528 measured |
| LMArena Text: non-English | 1412 | Not measured | Only R1 0528 measured |
| LMArena Text: Chinese | 1442 | Not measured | Only R1 0528 measured |
| LMArena Text: German | 1404 | Not measured | Only R1 0528 measured |
| LMArena Text: Japanese | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: Korean | 1360 | Not measured | Only R1 0528 measured |
| LMArena Text: French | 1417 | Not measured | Only R1 0528 measured |
| LMArena Text: Spanish | 1411 | Not measured | Only R1 0528 measured |
| LMArena Text: Russian | 1423 | Not measured | Only R1 0528 measured |
| LMArena Text: Polish | 1428 | Not measured | Only R1 0528 measured |
| LMArena Text: multi-turn | 1405 | Not measured | Only R1 0528 measured |
| LMArena Text: longer queries | 1391 | Not measured | Only R1 0528 measured |
| LMArena Text: writing and language | 1402 | Not measured | Only R1 0528 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only R1 0528 measured |
| Neither model has a result on the other 24 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
Mistral Nemo costs less
Repository review
50K input + 3K output tokens
Mistral Nemo costs less
Agent loop
200K input + 10K output tokens
Mistral Nemo 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 Nemo |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | Open weights |
| Released | May 28, 2025 | July 19, 2024 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.50 | $0.15 |
| Output $/Mtok | $2.18 | $0.15 |
| 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 Nemo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick R1 0528 for a larger context window (164K against 131K tokens).
Which is cheaper, R1 0528 or Mistral Nemo?
R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; Mistral Nemo costs $0.15 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, R1 0528 or Mistral Nemo?
R1 0528, with 164K tokens against 131K.
Has any benchmark tested both R1 0528 and Mistral Nemo?
No benchmark has tested both yet. R1 0528 has results on Aider polyglot, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Mistral Nemo on Epoch Capabilities Index, MMLU and DTBench. Compare them on price and context instead. Data as of October 21, 2026.