DeepSeek V4 Flash 0423 vs Mistral Nemo
Updated October 21, 2026. 2 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
DeepSeek V4 Flash 0423 has the edge where the context window is larger, 1.05M against 131K tokens.
- Shared results
- 2
- DeepSeek V4 Flash 0423 only
- 46
- Mistral Nemo only
- 1
- Like-for-like categories
- 2 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 DeepSeek V4 Flash 0423 has a AA Intelligence Index result, 24.4.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Flash 0423 has a AA Agentic Index result, 26.3.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4 Flash 0423 has a Design Arena: websites result, 1208.
Directional only
Long documents
Listed context window
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Nemo
DeepSeek V4 Flash 0423 $0.0003, Mistral Nemo $0.0002 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4 Flash 0423 not benchmarked, Mistral Nemo not benchmarked.
- Widest category
- General, 37.8 points apart on average over 1 shared benchmark.
- Widest single gap
- DTBench: DeepSeek V4 Flash 0423 86.4% against Mistral Nemo 48.6%, 37.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0423 $0.28 against Mistral Nemo $0.15 per million output tokens, 1.9x apart.
- Context window
- DeepSeek V4 Flash 0423 takes 1.05M tokens, Mistral Nemo 131K.
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 | DeepSeek V4 Flash 0423 | Bars | Mistral Nemo | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | Leader |
|---|---|---|---|
| LMArena WebDev | 1582 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| AA Coding Index | 52.0 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| WeirdML | 45.6% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| ALE-Bench | 678.2 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: coding | 1457 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena WebDev: React | 1585 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena WebDev: HTML | 1558 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.4 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Epoch Capabilities Index | 146.1 | 118.7 | DeepSeek V4 Flash 0423 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | Leader |
|---|---|---|---|
| SimpleBench | 46.3% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: hard prompts | 1444 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: expert | 1441 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: hard prompts in English | 1449 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | Leader |
|---|---|---|---|
| LMArena Text: math | 1426 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: mathematical work | 1432 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | Leader |
|---|---|---|---|
| MMLU | Not measured | 65.3% | Only Mistral Nemo measured |
| LMArena Text: science | 1446 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: medicine and healthcare | 1448 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: legal and government | 1439 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: business and finance | 1425 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | Leader |
|---|---|---|---|
| Design Arena: websites | 1208 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: UI components | 1165 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: game dev | 1197 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: data viz | 1127 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: 3D | 1189 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: all code categories | 1205 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: SVG | 1155 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: ASCII art | 1109 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | Leader |
|---|---|---|---|
| DTBench | 86.4% | 48.6% | DeepSeek V4 Flash 0423 |
| LMCA | 35.9% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text | 1432 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: instruction following | 1421 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: creative writing | 1403 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: English | 1441 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: non-English | 1419 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Chinese | 1463 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: German | 1419 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Japanese | 1389 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Korean | 1383 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: French | 1439 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Russian | 1427 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: Polish | 1436 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: multi-turn | 1449 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: longer queries | 1434 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: writing and language | 1410 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Mistral Nemo | 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
Mistral Nemo costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0423 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 0423 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 V4 Flash 0423 | Mistral Nemo |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | July 19, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.14 | $0.15 |
| Output $/Mtok | $0.28 | $0.15 |
| 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 DeepSeek V4 Flash 0423 better than Mistral Nemo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. DeepSeek V4 Flash 0423 has the edge where the context window is larger, 1.05M against 131K tokens.
Which is cheaper, DeepSeek V4 Flash 0423 or Mistral Nemo?
DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 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, DeepSeek V4 Flash 0423 or Mistral Nemo?
DeepSeek V4 Flash 0423, with 1.05M tokens against 131K.
How many benchmarks is the DeepSeek V4 Flash 0423 vs Mistral Nemo comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and Mistral Nemo's on 0. Where only one model has a result, the page says so instead of naming a winner.