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Llama 3.3 70B Instruct vs Qwen3.7 Plus

Qwen3.7 Plus leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 25.2 to 7.7, and Llama 3.3 70B Instruct costs 1.8x less per output token ($0.72 vs $1.28 per million). Updated October 21, 2026; 41 benchmarks have a result for both.

Replace Llama 3.3 70B Instruct Replace Qwen3.7 Plus
Llama 3.3 70B InstructMeta Llama
Coding
No data
Overall
26.0
Output $/M
$0.72
0
Qwen3.7 PlusQwen
Coding
33.2
Overall
54.0
Output $/M
$1.28
41
vs boards won of 41 shared

Llama 3.3 70B Instruct or Qwen3.7 Plus: which to pick

Only Qwen3.7 Plus has a coding score, 33.2 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding.

Pick Llama 3.3 70B Instruct for output tokens 1.8x cheaper ($0.72 vs $1.28 per million).

Model ALlama 3.3 70B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.7 PlusQwen33.2/1002 independent benchmarksNo cost per solved task
Shared results
41
Llama 3.3 70B Instruct only
11
Qwen3.7 Plus only
34
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
Llama 3.3 70B Instruct not benchmarked, Qwen3.7 Plus 33.2 from 2 benchmarks.
Widest category
Agentic work, 66.4 points apart on average over 1 shared benchmark.
Widest single gap
OTIS Mock AIME: Llama 3.3 70B Instruct 5.1% against Qwen3.7 Plus 93.3%, 88.2 points apart on a 0 to 100 scale.
Output price
Llama 3.3 70B Instruct $0.72 against Qwen3.7 Plus $1.28 per million output tokens, 1.8x apart.
Context window
Llama 3.3 70B Instruct takes 131K tokens, Qwen3.7 Plus 1M.

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
CategoryLlama 3.3 70B InstructBarsQwen3.7 PlusTies
Coding04
Intelligence02
Agentic work01
Reasoning06
Math03
Knowledge04
General021
All shared boards041
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
AA Coding Index11.955.9Qwen3.7 Plus
Vibe Code Bench (Vals AI)Not measured46.4%Only Qwen3.7 Plus measured
Code migration (Vals AI)Not measured12.9%Only Qwen3.7 Plus measured
Terminal-Bench 2.1 (Vals AI)Not measured52.8%Only Qwen3.7 Plus measured
SkillsBench (Vals AI)Not measured54.3%Only Qwen3.7 Plus measured
LiveCodeBench (Artificial Analysis run)28.8%Not measuredOnly Llama 3.3 70B Instruct measured
SciCodeNot measured46.1%Only Qwen3.7 Plus measured
Terminal-Bench Hard (Artificial Analysis run)3.0%47.0%Qwen3.7 Plus
FrontierCodeNot measured10.2%Only Qwen3.7 Plus measured
WeirdML14.4%Not measuredOnly Llama 3.3 70B Instruct measured
LMArena Text: coding12681473Qwen3.7 Plus
LMArena Text: software and IT12731473Qwen3.7 Plus
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
AA Intelligence Index7.725.2Qwen3.7 Plus
Epoch Capabilities Index127.3147.0Qwen3.7 Plus
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
AA Agentic IndexNot measured17.5Only Qwen3.7 Plus measured
Finance Agent (Vals AI)Not measured38.2%Only Qwen3.7 Plus measured
Tax Agent Bench (Vals AI)Not measured10.5%Only Qwen3.7 Plus measured
Legal Research Bench (Vals AI)Not measured16.4%Only Qwen3.7 Plus measured
Excel Modeling Benchmark (Vals AI)Not measured49.3%Only Qwen3.7 Plus measured
τ²-Bench Telecom26.6%93.0%Qwen3.7 Plus
OSWorld 2.0Not measured2.8%Only Qwen3.7 Plus measured
BALROG23.0%Not measuredOnly Llama 3.3 70B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)31.9%Not measuredOnly Llama 3.3 70B Instruct measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
GPQA Diamond (Artificial Analysis run)49.8%90.0%Qwen3.7 Plus
Humanity's Last Exam (Artificial Analysis run)3.6%35.6%Qwen3.7 Plus
GPQA Diamond (Epoch AI run)47.4%87.9%Qwen3.7 Plus
SimpleBench19.9%Not measuredOnly Llama 3.3 70B Instruct measured
Mystery game puzzlesNot measured17.0%Only Qwen3.7 Plus measured
LMArena Text: hard prompts12571460Qwen3.7 Plus
LMArena Text: expert12251466Qwen3.7 Plus
LMArena Text: hard prompts in English12771460Qwen3.7 Plus
LMArena Vision: diagramsNot measured1292Only Qwen3.7 Plus measured
LMArena Vision: homeworkNot measured1295Only Qwen3.7 Plus measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
AA Math Index7.7Not measuredOnly Llama 3.3 70B Instruct measured
AIME 2025 (Artificial Analysis run)7.7%Not measuredOnly Llama 3.3 70B Instruct measured
OTIS Mock AIME5.1%93.3%Qwen3.7 Plus
FrontierMath, tiers 1 to 3Not measured34.4%Only Qwen3.7 Plus measured
MATH level 541.6%Not measuredOnly Llama 3.3 70B Instruct measured
LMArena Text: math12671467Qwen3.7 Plus
LMArena Text: mathematical work12671470Qwen3.7 Plus
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
MortgageTax (Vals AI)Not measured66.2%Only Qwen3.7 Plus measured
MMLU Pro (Artificial Analysis run)71.3%Not measuredOnly Llama 3.3 70B Instruct measured
MMLU86.3%Not measuredOnly Llama 3.3 70B Instruct measured
LMArena Text: science12841464Qwen3.7 Plus
LMArena Text: medicine and healthcare12751464Qwen3.7 Plus
LMArena Text: legal and government12841451Qwen3.7 Plus
LMArena Text: business and finance12431435Qwen3.7 Plus
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 Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
Design Arena: websitesNot measured1269Only Qwen3.7 Plus measured
Design Arena: UI componentsNot measured1261Only Qwen3.7 Plus measured
Design Arena: game devNot measured1262Only Qwen3.7 Plus measured
Design Arena: data vizNot measured1276Only Qwen3.7 Plus measured
Design Arena: 3DNot measured1239Only Qwen3.7 Plus measured
Design Arena: all code categoriesNot measured1269Only Qwen3.7 Plus measured
Design Arena: SVGNot measured1208Only Qwen3.7 Plus measured
Design Arena: ASCII artNot measured1143Only Qwen3.7 Plus measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
General benchmarks for Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
SAGE (Vals AI)Not measured39.3%Only Qwen3.7 Plus measured
Vals Multimodal IndexNot measured53.9%Only Qwen3.7 Plus measured
IFBench47.1%78.0%Qwen3.7 Plus
Long context reasoning (AA-LCR)15.7%73.0%Qwen3.7 Plus
DTBench59.5%84.0%Qwen3.7 Plus
LMCA17.5%37.6%Qwen3.7 Plus
Fiction.LiveBench33.3%Not measuredOnly Llama 3.3 70B Instruct measured
LMArena Text12741454Qwen3.7 Plus
LMArena Text: instruction following12421439Qwen3.7 Plus
LMArena Text: creative writing12501438Qwen3.7 Plus
LMArena VisionNot measured1278Only Qwen3.7 Plus measured
LMArena Text: English13031458Qwen3.7 Plus
LMArena Text: non-English12361445Qwen3.7 Plus
LMArena Text: Chinese12171509Qwen3.7 Plus
LMArena Text: German12511471Qwen3.7 Plus
LMArena Text: Japanese11501412Qwen3.7 Plus
LMArena Text: Korean11421413Qwen3.7 Plus
LMArena Text: French12821472Qwen3.7 Plus
LMArena Text: Spanish12691457Qwen3.7 Plus
LMArena Text: Russian12521457Qwen3.7 Plus
LMArena Text: Polish12591449Qwen3.7 Plus
LMArena Text: multi-turn12801459Qwen3.7 Plus
LMArena Text: longer queries12551454Qwen3.7 Plus
LMArena Text: writing and language12551437Qwen3.7 Plus
LMArena Text: entertainment and media12411422Qwen3.7 Plus
LMArena Vision: EnglishNot measured1280Only Qwen3.7 Plus measured
LMArena Vision: ChineseNot measured1332Only Qwen3.7 Plus measured
LMArena Vision: reading textNot measured1288Only Qwen3.7 Plus measured
LMArena Vision: humorNot measured1261Only Qwen3.7 Plus measured
LMArena Vision: creative writingNot measured1258Only Qwen3.7 Plus measured
LMArena DocumentNot measured1444Only Qwen3.7 Plus measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Llama 3.3 70B Instruct and Qwen3.7 Plus
BenchmarkLlama 3.3 70B InstructQwen3.7 PlusLeader
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

Llama 3.3 70B Instruct$0.0008Fits in one request
Qwen3.7 Plus$0.0010Fits in one request

Llama 3.3 70B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.3 70B Instruct$0.025Fits in one request
Qwen3.7 Plus$0.020Fits in one request

Qwen3.7 Plus costs less

Agent loop

200K input + 10K output tokens

Llama 3.3 70B Instruct$0.097Exceeds the context window
Qwen3.7 Plus$0.077Fits in one request

Qwen3.7 Plus 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 Llama 3.3 70B Instruct and Qwen3.7 Plus
SpecificationMeta Llama 3.3 70B InstructQwen3.7 Plus
MakerMeta LlamaQwen
WeightsOpen weightsNo public weights listed
ReleasedDecember 6, 2024June 3, 2026
Context window131K1M
Input $/Mtok$0.45$0.32
Output $/Mtok$0.72$1.28
Coding indexNot benchmarked33.2
Independent measurements behind it02
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.3 70B Instruct better than Qwen3.7 Plus for coding?

Only Qwen3.7 Plus has a coding score, 33.2 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 1.8x cheaper ($0.72 vs $1.28 per million).

Which is cheaper, Llama 3.3 70B Instruct or Qwen3.7 Plus?

Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for neither of them.

Which has the bigger context window, Llama 3.3 70B Instruct or Qwen3.7 Plus?

Qwen3.7 Plus, with 1M tokens against 131K.

How many benchmarks is the Llama 3.3 70B Instruct vs Qwen3.7 Plus comparison based on?

41 benchmarks have a result for both models as of October 21, 2026. Llama 3.3 70B Instruct's coding index rests on 0 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.

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