Enter opens the highlighted result. Esc closes.

Llama 3.1 8B Instruct vs Qwen3.5-35B-A3B

Qwen3.5-35B-A3B leads Llama 3.1 8B Instruct on the Artificial Analysis Intelligence Index, 19.3 to 6.9, and Llama 3.1 8B Instruct costs 16.3x less per output token ($0.08 vs $1.30 per million). Updated October 22, 2026; 39 benchmarks have a result for both.

Replace Llama 3.1 8B Instruct Replace Qwen3.5-35B-A3B
Llama 3.1 8B InstructMeta Llama
Coding
No data
Overall
17.9
Output $/M
$0.08
0
Qwen3.5-35B-A3BQwen
Coding
No data
Overall
53.5
Output $/M
$1.30
39
vs boards won of 39 shared

Llama 3.1 8B Instruct or Qwen3.5-35B-A3B: which to pick

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

Pick Llama 3.1 8B Instruct for output tokens 16.3x cheaper ($0.08 vs $1.30 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model ALlama 3.1 8B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-35B-A3BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
39
Llama 3.1 8B Instruct only
12
Qwen3.5-35B-A3B only
6
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 3.1 8B Instruct not benchmarked, Qwen3.5-35B-A3B not benchmarked.
Widest category
Agentic work, 72.8 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Llama 3.1 8B Instruct 16.4% against Qwen3.5-35B-A3B 89.2%, 72.8 points apart on a 0 to 100 scale.
Output price
Llama 3.1 8B Instruct $0.08 against Qwen3.5-35B-A3B $1.30 per million output tokens, 16.3x apart.
Context window
Llama 3.1 8B Instruct takes 131K tokens, Qwen3.5-35B-A3B 262K.

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 3.1 8B InstructBarsQwen3.5-35B-A3BTies
Coding03
Intelligence02
Agentic work01
Reasoning06
Math03
Knowledge04
General020
All shared boards039
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Llama 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
LMArena WebDevNot measured1254Only Qwen3.5-35B-A3B measured
AA Coding Index5.4Not measuredOnly Llama 3.1 8B Instruct measured
LiveCodeBench (Artificial Analysis run)11.6%Not measuredOnly Llama 3.1 8B Instruct measured
Terminal-Bench Hard (Artificial Analysis run)0.8%26.5%Qwen3.5-35B-A3B
WeirdML1.7%Not measuredOnly Llama 3.1 8B Instruct measured
ALE-BenchNot measured221.8Only Qwen3.5-35B-A3B measured
LMArena Text: coding11951410Qwen3.5-35B-A3B
LMArena Text: software and IT11951415Qwen3.5-35B-A3B
LMArena WebDev: ReactNot measured1238Only Qwen3.5-35B-A3B measured
LMArena WebDev: HTMLNot measured1301Only Qwen3.5-35B-A3B measured
Neither model has a result on the other 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Llama 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
AA Intelligence Index6.919.3Qwen3.5-35B-A3B
Epoch Capabilities Index116.5142.5Qwen3.5-35B-A3B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Llama 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
τ²-Bench Telecom16.4%89.2%Qwen3.5-35B-A3B
BALROG15.1%Not measuredOnly Llama 3.1 8B Instruct measured
Vending-Bench 2Not measured$463Only Qwen3.5-35B-A3B measured
Berkeley Function Calling Leaderboard (BFCL)25.8%Not measuredOnly Llama 3.1 8B Instruct measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for Llama 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
GPQA Diamond (Artificial Analysis run)25.9%84.5%Qwen3.5-35B-A3B
Humanity's Last Exam (Artificial Analysis run)5.3%21.0%Qwen3.5-35B-A3B
GPQA Diamond (Epoch AI run)27.0%83.5%Qwen3.5-35B-A3B
LMArena Text: hard prompts11751400Qwen3.5-35B-A3B
LMArena Text: expert11441408Qwen3.5-35B-A3B
LMArena Text: hard prompts in English11961411Qwen3.5-35B-A3B
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for Llama 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
AA Math Index4.3Not measuredOnly Llama 3.1 8B Instruct measured
AIME 2025 (Artificial Analysis run)4.3%Not measuredOnly Llama 3.1 8B Instruct measured
OTIS Mock AIME1.7%70.0%Qwen3.5-35B-A3B
MATH level 522.9%Not measuredOnly Llama 3.1 8B Instruct measured
GSM8K82.4%Not measuredOnly Llama 3.1 8B Instruct measured
LMArena Text: math11791404Qwen3.5-35B-A3B
LMArena Text: mathematical work11801410Qwen3.5-35B-A3B
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 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
MMLU Pro (Artificial Analysis run)47.6%Not measuredOnly Llama 3.1 8B Instruct measured
MMLU56.1%Not measuredOnly Llama 3.1 8B Instruct measured
LMArena Text: science11931413Qwen3.5-35B-A3B
LMArena Text: medicine and healthcare11821421Qwen3.5-35B-A3B
LMArena Text: legal and government12061399Qwen3.5-35B-A3B
LMArena Text: business and finance11661394Qwen3.5-35B-A3B
Neither model has a result on the other 12 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Llama 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for Llama 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
IFBench28.6%72.5%Qwen3.5-35B-A3B
Long context reasoning (AA-LCR)18.0%72.0%Qwen3.5-35B-A3B
DTBench50.9%80.0%Qwen3.5-35B-A3B
LMCA5.4%29.5%Qwen3.5-35B-A3B
PIQA81.2%Not measuredOnly Llama 3.1 8B Instruct measured
LMArena Text11871395Qwen3.5-35B-A3B
LMArena Text: instruction following11591379Qwen3.5-35B-A3B
LMArena Text: creative writing11541347Qwen3.5-35B-A3B
LMArena Text: English12181407Qwen3.5-35B-A3B
LMArena Text: non-English11481378Qwen3.5-35B-A3B
LMArena Text: Chinese11511457Qwen3.5-35B-A3B
LMArena Text: German11441367Qwen3.5-35B-A3B
LMArena Text: Japanese10611326Qwen3.5-35B-A3B
LMArena Text: Korean10531355Qwen3.5-35B-A3B
LMArena Text: French11781412Qwen3.5-35B-A3B
LMArena Text: Spanish11691390Qwen3.5-35B-A3B
LMArena Text: Russian11581376Qwen3.5-35B-A3B
LMArena Text: PolishNot measured1385Only Qwen3.5-35B-A3B measured
LMArena Text: multi-turn11721390Qwen3.5-35B-A3B
LMArena Text: longer queries11811389Qwen3.5-35B-A3B
LMArena Text: writing and language11621365Qwen3.5-35B-A3B
LMArena Text: entertainment and media11451347Qwen3.5-35B-A3B
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 Llama 3.1 8B Instruct and Qwen3.5-35B-A3B
BenchmarkLlama 3.1 8B InstructQwen3.5-35B-A3BLeader
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 3.1 8B Instruct$0.0001Fits in one request
Qwen3.5-35B-A3B$0.0008Fits in one request

Llama 3.1 8B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.1 8B Instruct$0.0027Fits in one request
Qwen3.5-35B-A3B$0.012Fits in one request

Llama 3.1 8B Instruct costs less

Agent loop

200K input + 10K output tokens

Llama 3.1 8B Instruct$0.011Exceeds the context window
Qwen3.5-35B-A3B$0.045Fits in one request

Llama 3.1 8B Instruct 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 3.1 8B Instruct and Qwen3.5-35B-A3B
SpecificationMeta Llama 3.1 8B InstructQwen3.5-35B-A3B
MakerMeta LlamaQwen
WeightsOpen weightsOpen weights
ReleasedJuly 23, 2024February 25, 2026
Context window131K262K
Input $/Mtok$0.05$0.1625
Output $/Mtok$0.08$1.30
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.1 8B Instruct better than Qwen3.5-35B-A3B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.1 8B Instruct for output tokens 16.3x cheaper ($0.08 vs $1.30 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, Llama 3.1 8B Instruct or Qwen3.5-35B-A3B?

Llama 3.1 8B Instruct costs $0.05 per million input tokens and $0.08 per million output tokens; Qwen3.5-35B-A3B costs $0.1625 and $1.30. Cost per solved task is known for neither of them.

Which has the bigger context window, Llama 3.1 8B Instruct or Qwen3.5-35B-A3B?

Qwen3.5-35B-A3B, with 262K tokens against 131K.

How many benchmarks is the Llama 3.1 8B Instruct vs Qwen3.5-35B-A3B comparison based on?

39 benchmarks have a result for both models as of October 22, 2026. Llama 3.1 8B Instruct's coding index rests on 0 independent benchmarks and Qwen3.5-35B-A3B's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next