DeepSeek V4.1 Flash 1017 vs Mistral Nemo
Mistral Nemo costs 4.0x less per output token than DeepSeek V4.1 Flash 1017 ($0.15 vs $0.60 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- 70.3
- Overall
- 67.6
- Output $/M
- $0.60
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
DeepSeek V4.1 Flash 1017 or Mistral Nemo: which to pick
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; Mistral Nemo has none, so the two cannot be ranked on coding.
Pick Mistral Nemo for output tokens 4.0x cheaper ($0.15 vs $0.60 per million).
No benchmark has tested both yet. DeepSeek V4.1 Flash 1017 has results on LiveBench coding, LMArena WebDev and Terminal-Bench 4 (Vals AI); 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
Only DeepSeek V4.1 Flash 1017 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V4.1 Flash 1017 has a AA Intelligence Index result, 39.5.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4.1 Flash 1017 has a Design Arena: websites result, 1312.
Directional only
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
Long documents
Listed context window
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Nemo
DeepSeek V4.1 Flash 1017 $0.0004, Mistral Nemo $0.0002 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, 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
- DeepSeek V4.1 Flash 1017 $0.60 against Mistral Nemo $0.15 per million output tokens, 4.0x apart.
- Context window
- DeepSeek V4.1 Flash 1017 takes 1.05M 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 Index9 boards measured
| Benchmark | DeepSeek V4.1 Flash 1017 | Mistral Nemo | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1664 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SciCode | 55.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| ALE-Bench | 1622.0 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: React | 1667 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: HTML | 1687 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | DeepSeek V4.1 Flash 1017 | Mistral Nemo | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Epoch Capabilities Index | Not measured | 118.7 | Only Mistral Nemo measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | DeepSeek V4.1 Flash 1017 | Mistral Nemo | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | 1230 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench browser | 76.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench long agentic | 50.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Finance Agent (Vals AI) | 57.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Tax Agent Bench (Vals AI) | 44.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | 61.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | DeepSeek V4.1 Flash 1017 | Mistral Nemo | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Epoch AI run) | 93.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | 49.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | DeepSeek V4.1 Flash 1017 | Mistral Nemo | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tiers 1 to 3 | 71.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tier 4 | 46.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | DeepSeek V4.1 Flash 1017 | Mistral Nemo | Leader |
|---|---|---|---|
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| MMLU | Not measured | 65.3% | Only Mistral Nemo measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | DeepSeek V4.1 Flash 1017 | Mistral Nemo | Leader |
|---|---|---|---|
| Design Arena: websites | 1312 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: UI components | 1300 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: game dev | 1362 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: data viz | 1335 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: 3D | 1372 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: all code categories | 1334 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: ASCII art | 1243 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | DeepSeek V4.1 Flash 1017 | Mistral Nemo | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Long context reasoning (AA-LCR) | 82.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| DTBench | Not measured | 48.6% | Only Mistral Nemo measured |
| Neither model has a result on the other 42 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 V4.1 Flash 1017 | Mistral Nemo |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | July 19, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.15 | $0.15 |
| Output $/Mtok | $0.60 | $0.15 |
| Coding index | 70.3 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than Mistral Nemo for coding?
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; Mistral Nemo has none, so the two cannot be ranked on coding. Pick Mistral Nemo for output tokens 4.0x cheaper ($0.15 vs $0.60 per million).
Which is cheaper, DeepSeek V4.1 Flash 1017 or Mistral Nemo?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 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 V4.1 Flash 1017 or Mistral Nemo?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 131K.
Has any benchmark tested both DeepSeek V4.1 Flash 1017 and Mistral Nemo?
No benchmark has tested both yet. DeepSeek V4.1 Flash 1017 has results on LiveBench coding, LMArena WebDev and Terminal-Bench 4 (Vals AI); Mistral Nemo on Epoch Capabilities Index, MMLU and DTBench. Compare them on price and context instead. Data as of October 21, 2026.