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DeepSeek V4 Flash 0423 vs Llama 3.1 70B Instruct

Updated October 21, 2026. 32 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V4 Flash 0423 Replace Llama 3.1 70B Instruct
DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
24.4
Output $/M
$0.28
32
Llama 3.1 70B InstructMeta Llama
Coding
No data
Overall
6.6
Output $/M
$0.56
0
vs boards won of 32 shared

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 output tokens cost 2.0x less, and where the context window is larger, 1.05M against 131K tokens.

Model ADeepSeek V4 Flash 0423DeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BLlama 3.1 70B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
32
DeepSeek V4 Flash 0423 only
16
Llama 3.1 70B Instruct only
15
Like-for-like categories
6 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.

At a glance

Coding index
DeepSeek V4 Flash 0423 not benchmarked, Llama 3.1 70B Instruct not benchmarked.
Widest category
Reasoning, 34.6 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena Text: Japanese: DeepSeek V4 Flash 0423 1389 against Llama 3.1 70B Instruct 1132, 42.8 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.28 against Llama 3.1 70B Instruct $0.56 per million output tokens, 2.0x apart.
Context window
DeepSeek V4 Flash 0423 takes 1.05M tokens, Llama 3.1 70B Instruct 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 Flash 0423BarsLlama 3.1 70B InstructTies
Coding30
Intelligence20
Reasoning30
Math20
Knowledge40
General180
All shared boards320
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
LMArena WebDev1582Not measuredOnly DeepSeek V4 Flash 0423 measured
AA Coding Index52.0Not measuredOnly DeepSeek V4 Flash 0423 measured
LiveCodeBench (Artificial Analysis run)Not measured23.2%Only Llama 3.1 70B Instruct measured
Terminal-Bench Hard (Artificial Analysis run)Not measured3.0%Only Llama 3.1 70B Instruct measured
WeirdML45.6%9.0%DeepSeek V4 Flash 0423
ALE-Bench678.2Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: coding14571260DeepSeek V4 Flash 0423
LMArena Text: software and IT14531260DeepSeek V4 Flash 0423
LMArena WebDev: React1585Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena WebDev: HTML1558Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
AA Intelligence Index24.46.6DeepSeek V4 Flash 0423
Epoch Capabilities Index146.1125.9DeepSeek V4 Flash 0423
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Agentic work benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Flash 0423 measured
τ²-Bench TelecomNot measured15.2%Only Llama 3.1 70B Instruct measured
BALROGNot measured27.9%Only Llama 3.1 70B Instruct measured
TheAgentCompanyNot measured6.9%Only Llama 3.1 70B Instruct measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
GPQA Diamond (Artificial Analysis run)Not measured40.9%Only Llama 3.1 70B Instruct measured
Humanity's Last Exam (Artificial Analysis run)Not measured4.5%Only Llama 3.1 70B Instruct measured
GPQA Diamond (Epoch AI run)Not measured44.2%Only Llama 3.1 70B Instruct measured
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: hard prompts14441241DeepSeek V4 Flash 0423
LMArena Text: expert14411209DeepSeek V4 Flash 0423
LMArena Text: hard prompts in English14491261DeepSeek V4 Flash 0423
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
AA Math IndexNot measured4.0Only Llama 3.1 70B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured4.0%Only Llama 3.1 70B Instruct measured
OTIS Mock AIMENot measured3.6%Only Llama 3.1 70B Instruct measured
MATH level 5Not measured36.7%Only Llama 3.1 70B Instruct measured
LMArena Text: math14261252DeepSeek V4 Flash 0423
LMArena Text: mathematical work14321253DeepSeek V4 Flash 0423
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
MMLU Pro (Artificial Analysis run)Not measured67.6%Only Llama 3.1 70B Instruct measured
MMLUNot measured80.1%Only Llama 3.1 70B Instruct measured
LMArena Text: science14461276DeepSeek V4 Flash 0423
LMArena Text: medicine and healthcare14481257DeepSeek V4 Flash 0423
LMArena Text: legal and government14391283DeepSeek V4 Flash 0423
LMArena Text: business and finance14251235DeepSeek V4 Flash 0423
Neither model has a result on the other 11 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
Design Arena: websites1208Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: UI components1165Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: game dev1197Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: data viz1127Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: 3D1189Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: all code categories1205Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: SVG1155Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: ASCII art1109Not measuredOnly 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 index180
General benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
IFBenchNot measured34.4%Only Llama 3.1 70B Instruct measured
DTBench86.4%60.0%DeepSeek V4 Flash 0423
LMCA35.9%14.8%DeepSeek V4 Flash 0423
LMArena Text14321261DeepSeek V4 Flash 0423
LMArena Text: instruction following14211231DeepSeek V4 Flash 0423
LMArena Text: creative writing14031232DeepSeek V4 Flash 0423
LMArena Text: English14411294DeepSeek V4 Flash 0423
LMArena Text: non-English14191219DeepSeek V4 Flash 0423
LMArena Text: Chinese14631215DeepSeek V4 Flash 0423
LMArena Text: German14191222DeepSeek V4 Flash 0423
LMArena Text: Japanese13891132DeepSeek V4 Flash 0423
LMArena Text: Korean13831140DeepSeek V4 Flash 0423
LMArena Text: French14391262DeepSeek V4 Flash 0423
LMArena Text: Spanish14361253DeepSeek V4 Flash 0423
LMArena Text: Russian14271234DeepSeek V4 Flash 0423
LMArena Text: Polish1436Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: multi-turn14491256DeepSeek V4 Flash 0423
LMArena Text: longer queries14341241DeepSeek V4 Flash 0423
LMArena Text: writing and language14101236DeepSeek V4 Flash 0423
LMArena Text: entertainment and media14001230DeepSeek V4 Flash 0423
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.1 70B InstructLeader
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 Flash 0423$0.0003Fits in one request
Llama 3.1 70B Instruct$0.0008Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0078Fits in one request
Llama 3.1 70B Instruct$0.030Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.031Fits in one request
Llama 3.1 70B Instruct$0.118Exceeds the context window

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.

Specifications of DeepSeek V4 Flash 0423 and Llama 3.1 70B Instruct
SpecificationDeepSeek V4 Flash 0423Meta Llama 3.1 70B Instruct
MakerDeepSeekMeta Llama
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026July 23, 2024
Context window1.05M131K
Input $/Mtok$0.14$0.56
Output $/Mtok$0.28$0.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than Llama 3.1 70B Instruct 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 output tokens cost 2.0x less, and where the context window is larger, 1.05M against 131K tokens.

Which is cheaper, DeepSeek V4 Flash 0423 or Llama 3.1 70B Instruct?

DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 per million output tokens; Llama 3.1 70B Instruct costs $0.56 and $0.56. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V4 Flash 0423 or Llama 3.1 70B Instruct?

DeepSeek V4 Flash 0423, with 1.05M tokens against 131K.

How many benchmarks is the DeepSeek V4 Flash 0423 vs Llama 3.1 70B Instruct comparison based on?

32 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 Llama 3.1 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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