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DeepSeek V4.1 Flash vs Mistral Medium 3.1

DeepSeek V4.1 Flash leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 24.7 to 9.2, and DeepSeek V4.1 Flash costs 2.6x less per output token ($0.78 vs $2.00 per million). Updated October 21, 2026; 52 benchmarks have a result for both.

DeepSeek V4.1 FlashDeepSeek
Coding
62.5
Overall
24.7
Output $/M
$0.78
52
Mistral Medium 3.1Mistral AI
Coding
No data
Overall
9.2
Output $/M
$2.00
0
vs boards won of 52 shared

DeepSeek V4.1 Flash or Mistral Medium 3.1: which to pick

Only DeepSeek V4.1 Flash has a coding score, 62.5 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Shared results
52
DeepSeek V4.1 Flash only
49
Mistral Medium 3.1 only
22
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.

At a glance

Coding index
DeepSeek V4.1 Flash 62.5 from 5 benchmarks, Mistral Medium 3.1 not benchmarked.
Widest category
Web design, 27.9 points apart on average over 8 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): DeepSeek V4.1 Flash 84.0% against Mistral Medium 3.1 42.7%, 41.3 points apart on a 0 to 100 scale.
Output price
DeepSeek V4.1 Flash $0.78 against Mistral Medium 3.1 $2.00 per million output tokens, 2.6x apart.
Context window
DeepSeek V4.1 Flash takes 1.05M tokens, Mistral Medium 3.1 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.

Boards won, where both have a result
CategoryDeepSeek V4.1 FlashBarsMistral Medium 3.1Ties
Coding40
Intelligence10
Reasoning60
Math20
Knowledge50
Web design80
General260
All shared boards520
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
LiveBench coding90.2%Not measuredOnly DeepSeek V4.1 Flash measured
LMArena WebDev1618Not measuredOnly DeepSeek V4.1 Flash measured
AA Coding IndexNot measured20.5Only Mistral Medium 3.1 measured
LiveCodeBench (Vals AI)Not measured44.8%Only Mistral Medium 3.1 measured
Terminal-Bench 4 (Vals AI)19.7%Not measuredOnly DeepSeek V4.1 Flash measured
Vibe Code Bench (Vals AI)84.7%Not measuredOnly DeepSeek V4.1 Flash measured
Code migration (Vals AI)45.6%Not measuredOnly DeepSeek V4.1 Flash measured
ProgramBench (Vals AI)0.5%Not measuredOnly DeepSeek V4.1 Flash measured
IOI (Vals AI)40.3%Not measuredOnly DeepSeek V4.1 Flash measured
Terminal-Bench 2.1 (Vals AI)74.5%Not measuredOnly DeepSeek V4.1 Flash measured
SWE-bench Multimodal (Vals AI)46.5%Not measuredOnly DeepSeek V4.1 Flash measured
Vibe Code Bench 1-100 (Vals AI)16.4%Not measuredOnly DeepSeek V4.1 Flash measured
SkillsBench (Vals AI)69.8%Not measuredOnly DeepSeek V4.1 Flash measured
CyberBench (Vals AI)73.7%Not measuredOnly DeepSeek V4.1 Flash measured
LiveCodeBench (Artificial Analysis run)Not measured40.6%Only Mistral Medium 3.1 measured
SciCode51.9%32.4%DeepSeek V4.1 Flash
Terminal-Bench Hard (Artificial Analysis run)Not measured10.6%Only Mistral Medium 3.1 measured
FrontierCode43.9%Not measuredOnly DeepSeek V4.1 Flash measured
ALE-Bench1092.3210.2DeepSeek V4.1 Flash
LMArena Text: coding15021433DeepSeek V4.1 Flash
LMArena Text: software and IT14961436DeepSeek V4.1 Flash
LMArena WebDev: React1628Not measuredOnly DeepSeek V4.1 Flash measured
LMArena WebDev: HTML1612Not measuredOnly DeepSeek V4.1 Flash measured
LMArena WebDev: from an image1606Not measuredOnly DeepSeek V4.1 Flash measured
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
AA Intelligence Index24.79.2DeepSeek V4.1 Flash
Epoch Capabilities Index155.9Not measuredOnly DeepSeek V4.1 Flash measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
Agentic work benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
AA Agentic IndexNot measured1.9Only Mistral Medium 3.1 measured
LiveBench research86.8%Not measuredOnly DeepSeek V4.1 Flash measured
LiveBench browser84.0%Not measuredOnly DeepSeek V4.1 Flash measured
LiveBench long agentic40.1%Not measuredOnly DeepSeek V4.1 Flash measured
SREBench (Vals AI)0.8%Not measuredOnly DeepSeek V4.1 Flash measured
Finance Agent (Vals AI)53.5%Not measuredOnly DeepSeek V4.1 Flash measured
Tax Agent Bench (Vals AI)26.6%Not measuredOnly DeepSeek V4.1 Flash measured
Legal Research Bench (Vals AI)41.4%Not measuredOnly DeepSeek V4.1 Flash measured
Excel Modeling Benchmark (Vals AI)57.2%Not measuredOnly DeepSeek V4.1 Flash measured
BioMysteryBench (Vals AI)67.8%Not measuredOnly DeepSeek V4.1 Flash measured
Terminal-Bench-Science (Vals AI)0.0%Not measuredOnly DeepSeek V4.1 Flash measured
Vals Science Index29.6%Not measuredOnly DeepSeek V4.1 Flash measured
τ²-Bench TelecomNot measured40.6%Only Mistral Medium 3.1 measured
APEX-Agents39.5%Not measuredOnly DeepSeek V4.1 Flash measured
Neither model has a result on the other 25 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
LiveBench reasoning86.7%Not measuredOnly DeepSeek V4.1 Flash measured
MysteryMechanism (Vals AI)21.2%Not measuredOnly DeepSeek V4.1 Flash measured
MMMU Pro (Vals AI)Not measured63.0%Only Mistral Medium 3.1 measured
GPQA Diamond (Artificial Analysis run)Not measured58.8%Only Mistral Medium 3.1 measured
Humanity's Last Exam (Artificial Analysis run)39.2%4.7%DeepSeek V4.1 Flash
GPQA Diamond (Epoch AI run)89.8%Not measuredOnly DeepSeek V4.1 Flash measured
ARC-AGI94.5%Not measuredOnly DeepSeek V4.1 Flash measured
ARC-AGI-272.9%Not measuredOnly DeepSeek V4.1 Flash measured
Chess puzzles48.0%Not measuredOnly DeepSeek V4.1 Flash measured
Mystery game puzzles43.0%Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Text: hard prompts14831426DeepSeek V4.1 Flash
LMArena Text: expert15011408DeepSeek V4.1 Flash
LMArena Text: hard prompts in English14881435DeepSeek V4.1 Flash
LMArena Vision: diagrams12971175DeepSeek V4.1 Flash
LMArena Vision: homework13071165DeepSeek V4.1 Flash
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
AIMENot measured42.3%Only Mistral Medium 3.1 measured
MATH 500 (Vals AI)Not measured87.0%Only Mistral Medium 3.1 measured
MGSM (Vals AI)Not measured90.9%Only Mistral Medium 3.1 measured
AA Math IndexNot measured38.3Only Mistral Medium 3.1 measured
AIME 2025 (Artificial Analysis run)Not measured38.3%Only Mistral Medium 3.1 measured
OTIS Mock AIME98.3%Not measuredOnly DeepSeek V4.1 Flash measured
FrontierMath, tiers 1 to 367.4%Not measuredOnly DeepSeek V4.1 Flash measured
FrontierMath, tier 426.8%Not measuredOnly DeepSeek V4.1 Flash measured
ProofBench54.0%Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Text: math14781408DeepSeek V4.1 Flash
LMArena Text: mathematical work14851412DeepSeek V4.1 Flash
Neither model has a result on the other 2 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
MMLU ProNot measured75.3%Only Mistral Medium 3.1 measured
LegalBench (Vals AI)83.3%62.0%DeepSeek V4.1 Flash
CorpFin (Vals AI)Not measured50.7%Only Mistral Medium 3.1 measured
TaxEval (Vals AI)Not measured70.3%Only Mistral Medium 3.1 measured
MortgageTax (Vals AI)Not measured36.5%Only Mistral Medium 3.1 measured
MedQA (Vals AI)Not measured78.2%Only Mistral Medium 3.1 measured
MedCode (Vals AI)41.2%Not measuredOnly DeepSeek V4.1 Flash measured
MMLU Pro (Artificial Analysis run)Not measured68.3%Only Mistral Medium 3.1 measured
SimpleQA Verified42.9%Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Text: science14691444DeepSeek V4.1 Flash
LMArena Text: medicine and healthcare14841449DeepSeek V4.1 Flash
LMArena Text: legal and government14431439DeepSeek V4.1 Flash
LMArena Text: business and finance14611418DeepSeek V4.1 Flash
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
Design Arena: websites13091133DeepSeek V4.1 Flash
Design Arena: UI components13151100DeepSeek V4.1 Flash
Design Arena: game dev13301074DeepSeek V4.1 Flash
Design Arena: data viz12901144DeepSeek V4.1 Flash
Design Arena: 3D13561081DeepSeek V4.1 Flash
Design Arena: all code categories13201120DeepSeek V4.1 Flash
Design Arena: SVG1244993DeepSeek V4.1 Flash
Design Arena: ASCII art1265997DeepSeek V4.1 Flash
LiveBench design79.2%Not measuredOnly DeepSeek V4.1 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index260
General benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
LiveBench data analysis79.3%Not measuredOnly DeepSeek V4.1 Flash measured
Public Benefits Bench (Vals AI)64.3%Not measuredOnly DeepSeek V4.1 Flash measured
SAGE (Vals AI)47.9%Not measuredOnly DeepSeek V4.1 Flash measured
MedScribe (Vals AI)85.5%Not measuredOnly DeepSeek V4.1 Flash measured
IFBenchNot measured39.8%Only Mistral Medium 3.1 measured
Long context reasoning (AA-LCR)84.0%42.7%DeepSeek V4.1 Flash
DTBench89.9%67.5%DeepSeek V4.1 Flash
LMCA47.0%19.1%DeepSeek V4.1 Flash
Surface Evolver Bench46.3%Not measuredOnly DeepSeek V4.1 Flash measured
Furniture assembly34.2%Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Text14641424DeepSeek V4.1 Flash
LMArena Text: instruction following14731398DeepSeek V4.1 Flash
LMArena Text: creative writing14371391DeepSeek V4.1 Flash
LMArena Vision12781172DeepSeek V4.1 Flash
LMArena Text: English14731438DeepSeek V4.1 Flash
LMArena Text: non-English14491408DeepSeek V4.1 Flash
LMArena Text: Chinese15011447DeepSeek V4.1 Flash
LMArena Text: German14701432DeepSeek V4.1 Flash
LMArena Text: Japanese14321379DeepSeek V4.1 Flash
LMArena Text: Korean14361380DeepSeek V4.1 Flash
LMArena Text: French14651460DeepSeek V4.1 Flash
LMArena Text: Spanish14591433DeepSeek V4.1 Flash
LMArena Text: Russian14671411DeepSeek V4.1 Flash
LMArena Text: Polish14571430DeepSeek V4.1 Flash
LMArena Text: multi-turn14611418DeepSeek V4.1 Flash
LMArena Text: longer queries14731406DeepSeek V4.1 Flash
LMArena Text: writing and language14501395DeepSeek V4.1 Flash
LMArena Text: entertainment and media14451390DeepSeek V4.1 Flash
LMArena Vision: English12881179DeepSeek V4.1 Flash
LMArena Vision: Chinese13271190DeepSeek V4.1 Flash
LMArena Vision: reading text12821175DeepSeek V4.1 Flash
LMArena Vision: humor12601168DeepSeek V4.1 Flash
LMArena Vision: captioningNot measured1145Only Mistral Medium 3.1 measured
LMArena Vision: recognizing thingsNot measured1147Only Mistral Medium 3.1 measured
LMArena Vision: creative writing12751200DeepSeek V4.1 Flash
Neither model has a result on the other 11 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V4.1 Flash and Mistral Medium 3.1
BenchmarkDeepSeek V4.1 FlashMistral Medium 3.1Leader
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

DeepSeek V4.1 Flash$0.0006Fits in one request
Mistral Medium 3.1$0.0014Fits in one request

DeepSeek V4.1 Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V4.1 Flash$0.013Fits in one request
Mistral Medium 3.1$0.026Fits in one request

DeepSeek V4.1 Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4.1 Flash$0.050Fits in one request
Mistral Medium 3.1$0.100Exceeds the context window

DeepSeek V4.1 Flash 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.

Specifications of DeepSeek V4.1 Flash and Mistral Medium 3.1
SpecificationDeepSeek V4.1 FlashMistral Medium 3.1
MakerDeepSeekMistral AI
WeightsOpen weightsNo public weights listed
ReleasedSeptember 10, 2026August 13, 2025
Context window1.05M131K
Input $/Mtok$0.21$0.40
Output $/Mtok$0.78$2.00
Coding index62.5Not benchmarked
Independent measurements behind it50
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4.1 Flash better than Mistral Medium 3.1 for coding?

Only DeepSeek V4.1 Flash has a coding score, 62.5 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, DeepSeek V4.1 Flash or Mistral Medium 3.1?

DeepSeek V4.1 Flash costs $0.21 per million input tokens and $0.78 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V4.1 Flash or Mistral Medium 3.1?

DeepSeek V4.1 Flash, with 1.05M tokens against 131K.

How many benchmarks is the DeepSeek V4.1 Flash vs Mistral Medium 3.1 comparison based on?

52 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash's coding index rests on 5 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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