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DeepSeek V4 Flash 0423 vs Llama 3.3 70B Instruct

DeepSeek V4 Flash 0423 leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 24.4 to 7.7, and DeepSeek V4 Flash 0423 costs 2.6x less per output token ($0.28 vs $0.72 per million). Updated October 21, 2026; 35 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0423 Replace Llama 3.3 70B Instruct
DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
No data
Output $/M
$0.28
35
Llama 3.3 70B InstructMeta Llama
Coding
No data
Overall
26.0
Output $/M
$0.72
0
vs boards won of 35 shared

DeepSeek V4 Flash 0423 or Llama 3.3 70B Instruct: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick DeepSeek V4 Flash 0423 for output tokens 2.6x cheaper ($0.28 vs $0.72 per million) and a larger context window (1.05M against 131K tokens).

Model ADeepSeek V4 Flash 0423DeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BLlama 3.3 70B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
35
DeepSeek V4 Flash 0423 only
14
Llama 3.3 70B Instruct only
17
Like-for-like categories
6 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 Flash 0423 not benchmarked, Llama 3.3 70B Instruct not benchmarked.
Widest category
Coding, 33.2 points apart on average over 4 shared benchmarks.
Widest single gap
LMArena Text: Chinese: DeepSeek V4 Flash 0423 1463 against Llama 3.3 70B Instruct 1217, 41.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.28 against Llama 3.3 70B Instruct $0.72 per million output tokens, 2.6x apart.
Context window
DeepSeek V4 Flash 0423 takes 1.05M tokens, Llama 3.3 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 3.3 70B InstructTies
Coding40
Intelligence20
Reasoning40
Math20
Knowledge40
General190
All shared boards350
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V4 Flash 0423 and Llama 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 70B InstructLeader
LMArena WebDev1582Not measuredOnly DeepSeek V4 Flash 0423 measured
AA Coding Index52.011.9DeepSeek V4 Flash 0423
LiveCodeBench (Artificial Analysis run)Not measured28.8%Only Llama 3.3 70B Instruct measured
Terminal-Bench Hard (Artificial Analysis run)Not measured3.0%Only Llama 3.3 70B Instruct measured
WeirdML45.6%14.4%DeepSeek V4 Flash 0423
ALE-Bench678.2Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: coding14571268DeepSeek V4 Flash 0423
LMArena Text: software and IT14531273DeepSeek 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 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Llama 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 70B InstructLeader
AA Intelligence Index24.47.7DeepSeek V4 Flash 0423
Epoch Capabilities Index146.1127.3DeepSeek 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 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 70B InstructLeader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Flash 0423 measured
τ²-Bench TelecomNot measured26.6%Only Llama 3.3 70B Instruct measured
BALROGNot measured23.0%Only Llama 3.3 70B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)Not measured31.9%Only Llama 3.3 70B Instruct measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Llama 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 70B InstructLeader
GPQA Diamond (Artificial Analysis run)Not measured49.8%Only Llama 3.3 70B Instruct measured
Humanity's Last Exam (Artificial Analysis run)Not measured3.6%Only Llama 3.3 70B Instruct measured
GPQA Diamond (Epoch AI run)Not measured47.4%Only Llama 3.3 70B Instruct measured
SimpleBench46.3%19.9%DeepSeek V4 Flash 0423
LMArena Text: hard prompts14441257DeepSeek V4 Flash 0423
LMArena Text: expert14411225DeepSeek V4 Flash 0423
LMArena Text: hard prompts in English14491277DeepSeek V4 Flash 0423
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V4 Flash 0423 measured
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 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 70B InstructLeader
AA Math IndexNot measured7.7Only Llama 3.3 70B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured7.7%Only Llama 3.3 70B Instruct measured
OTIS Mock AIMENot measured5.1%Only Llama 3.3 70B Instruct measured
MATH level 5Not measured41.6%Only Llama 3.3 70B Instruct measured
LMArena Text: math14261267DeepSeek V4 Flash 0423
LMArena Text: mathematical work14321267DeepSeek 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 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 70B InstructLeader
MMLU Pro (Artificial Analysis run)Not measured71.3%Only Llama 3.3 70B Instruct measured
MMLUNot measured86.3%Only Llama 3.3 70B Instruct measured
LMArena Text: science14461284DeepSeek V4 Flash 0423
LMArena Text: medicine and healthcare14481275DeepSeek V4 Flash 0423
LMArena Text: legal and government14391284DeepSeek V4 Flash 0423
LMArena Text: business and finance14251243DeepSeek V4 Flash 0423
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
Web design benchmarks for DeepSeek V4 Flash 0423 and Llama 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 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 index190
General benchmarks for DeepSeek V4 Flash 0423 and Llama 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 70B InstructLeader
IFBenchNot measured47.1%Only Llama 3.3 70B Instruct measured
Long context reasoning (AA-LCR)Not measured15.7%Only Llama 3.3 70B Instruct measured
DTBench86.4%59.5%DeepSeek V4 Flash 0423
LMCA35.9%17.5%DeepSeek V4 Flash 0423
Fiction.LiveBenchNot measured33.3%Only Llama 3.3 70B Instruct measured
LMArena Text14321274DeepSeek V4 Flash 0423
LMArena Text: instruction following14211242DeepSeek V4 Flash 0423
LMArena Text: creative writing14031250DeepSeek V4 Flash 0423
LMArena Text: English14411303DeepSeek V4 Flash 0423
LMArena Text: non-English14191236DeepSeek V4 Flash 0423
LMArena Text: Chinese14631217DeepSeek V4 Flash 0423
LMArena Text: German14191251DeepSeek V4 Flash 0423
LMArena Text: Japanese13891150DeepSeek V4 Flash 0423
LMArena Text: Korean13831142DeepSeek V4 Flash 0423
LMArena Text: French14391282DeepSeek V4 Flash 0423
LMArena Text: Spanish14361269DeepSeek V4 Flash 0423
LMArena Text: Russian14271252DeepSeek V4 Flash 0423
LMArena Text: Polish14361259DeepSeek V4 Flash 0423
LMArena Text: multi-turn14491280DeepSeek V4 Flash 0423
LMArena Text: longer queries14341255DeepSeek V4 Flash 0423
LMArena Text: writing and language14101255DeepSeek V4 Flash 0423
LMArena Text: entertainment and media14001241DeepSeek V4 Flash 0423
Neither model has a result on the other 24 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 3.3 70B Instruct
BenchmarkDeepSeek V4 Flash 0423Llama 3.3 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 3.3 70B Instruct$0.0008Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0073Fits in one request
Llama 3.3 70B Instruct$0.025Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.029Fits in one request
Llama 3.3 70B Instruct$0.097Exceeds 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 3.3 70B Instruct
SpecificationDeepSeek V4 Flash 0423Meta Llama 3.3 70B Instruct
MakerDeepSeekMeta Llama
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026December 6, 2024
Context window1.05M131K
Input $/Mtok$0.13$0.45
Output $/Mtok$0.28$0.72
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 3.3 70B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash 0423 for output tokens 2.6x cheaper ($0.28 vs $0.72 per million) and a larger context window (1.05M against 131K tokens).

Which is cheaper, DeepSeek V4 Flash 0423 or Llama 3.3 70B Instruct?

DeepSeek V4 Flash 0423 costs $0.13 per million input tokens and $0.28 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V4 Flash 0423 or Llama 3.3 70B Instruct?

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

How many benchmarks is the DeepSeek V4 Flash 0423 vs Llama 3.3 70B Instruct comparison based on?

35 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 3.3 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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