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Claude Opus 4.8 vs Qwen3.5-35B-A3B

Updated October 21, 2026. 44 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Claude Opus 4.8Anthropic
Coding
66.0
Overall
41.8
Output $/M
$25.00
43
Qwen3.5-35B-A3BQwen
Coding
7.7
Overall
19.3
Output $/M
$1.00
1
vs boards won of 44 shared

Decision reading

Pick Claude Opus 4.8 for coding: it scores 66.0 on the Vibe Coding Index against 7.7 for Qwen3.5-35B-A3B.

Pick Qwen3.5-35B-A3B when output tokens cost 25.0x less, or when the benchmark that matters is IFBench, where it leads.

Shared results
44
Claude Opus 4.8 only
82
Qwen3.5-35B-A3B only
1
Like-for-like categories
7 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
Claude Opus 4.8 66.0 from 2 benchmarks, Qwen3.5-35B-A3B 7.7 from 1 benchmark.
Widest category
Coding, 32.3 points apart on average over 6 shared benchmarks.
Widest single gap
LMArena WebDev: React: Claude Opus 4.8 1557 against Qwen3.5-35B-A3B 1238, 53.1 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.8 $25.00 against Qwen3.5-35B-A3B $1.00 per million output tokens, 25.0x apart.
Context window
Claude Opus 4.8 takes 1M 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
CategoryClaude Opus 4.8BarsQwen3.5-35B-A3BTies
Coding60
Intelligence20
Agentic work20
Reasoning60
Math30
Knowledge40
General201
All shared boards431
CodingLike-for-likeFour of these boards feed the Vibe Coding Index60
Coding benchmarks for Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-A3BLeader
LiveBench coding77.3%Not measuredOnly Claude Opus 4.8 measured
LMArena WebDev15561254Claude Opus 4.8
AA Coding Index74.3Not measuredOnly Claude Opus 4.8 measured
LiveCodeBench (Vals AI)87.8%Not measuredOnly Claude Opus 4.8 measured
SWE-bench Verified (Vals AI)88.6%Not measuredOnly Claude Opus 4.8 measured
Terminal-Bench 4 (Vals AI)23.2%Not measuredOnly Claude Opus 4.8 measured
Vibe Code Bench (Vals AI)82.7%Not measuredOnly Claude Opus 4.8 measured
Code migration (Vals AI)47.3%Not measuredOnly Claude Opus 4.8 measured
ProgramBench (Vals AI)1.0%Not measuredOnly Claude Opus 4.8 measured
Terminal-Bench 2.1 (Vals AI)71.9%Not measuredOnly Claude Opus 4.8 measured
SkillsBench (Vals AI)59.2%Not measuredOnly Claude Opus 4.8 measured
SciCode54.4%Not measuredOnly Claude Opus 4.8 measured
Terminal-Bench Hard (Artificial Analysis run)58.3%26.5%Claude Opus 4.8
FrontierCode46.5%Not measuredOnly Claude Opus 4.8 measured
GSO-Bench47.1%Not measuredOnly Claude Opus 4.8 measured
WeirdML82.9%Not measuredOnly Claude Opus 4.8 measured
ALE-BenchNot measured221.8Only Qwen3.5-35B-A3B measured
LMArena Text: coding14901410Claude Opus 4.8
LMArena Text: software and IT14881415Claude Opus 4.8
LMArena WebDev: React15571238Claude Opus 4.8
LMArena WebDev: HTML15431301Claude Opus 4.8
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-A3BLeader
AA Intelligence Index41.819.3Claude Opus 4.8
Epoch Capabilities Index159.0142.5Claude Opus 4.8
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-A3BLeader
AA Agentic Index41.9Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building web apps1233Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building full-stack apps1249Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building mobile apps1222Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building Android apps1237Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building Godot games1231Not measuredOnly Claude Opus 4.8 measured
Design Arena: agents building games1238Not measuredOnly Claude Opus 4.8 measured
Design Arena: HTML slides1221Not measuredOnly Claude Opus 4.8 measured
Design Arena: PowerPoint slides1227Not measuredOnly Claude Opus 4.8 measured
LiveBench research81.8%Not measuredOnly Claude Opus 4.8 measured
LiveBench browser96.0%Not measuredOnly Claude Opus 4.8 measured
LiveBench long agentic48.3%Not measuredOnly Claude Opus 4.8 measured
Finance Agent (Vals AI)53.9%Not measuredOnly Claude Opus 4.8 measured
Tax Agent Bench (Vals AI)30.0%Not measuredOnly Claude Opus 4.8 measured
Legal Research Bench (Vals AI)43.8%Not measuredOnly Claude Opus 4.8 measured
Excel Modeling Benchmark (Vals AI)69.4%Not measuredOnly Claude Opus 4.8 measured
Terminal-Bench-Science (Vals AI)4.3%Not measuredOnly Claude Opus 4.8 measured
Time Horizon Index (Vals AI)11.8%Not measuredOnly Claude Opus 4.8 measured
τ²-Bench Telecom94.4%89.2%Claude Opus 4.8
APEX-Agents48.9%Not measuredOnly Claude Opus 4.8 measured
Remote Labor Index8.3%Not measuredOnly Claude Opus 4.8 measured
OSWorld 2.020.6%Not measuredOnly Claude Opus 4.8 measured
DeepResearch Bench50.2%Not measuredOnly Claude Opus 4.8 measured
PostTrainBench33.8%Not measuredOnly Claude Opus 4.8 measured
Vending-Bench 2$5,787$463Claude Opus 4.8
LMArena Search1204Not measuredOnly Claude Opus 4.8 measured
Neither model has a result on the other 13 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-A3BLeader
GPQA Diamond92.4%Not measuredOnly Claude Opus 4.8 measured
LiveBench reasoning89.2%Not measuredOnly Claude Opus 4.8 measured
MMMU Pro (Vals AI)86.6%Not measuredOnly Claude Opus 4.8 measured
GPQA Diamond (Artificial Analysis run)92.0%84.5%Claude Opus 4.8
Humanity's Last Exam (Artificial Analysis run)48.7%21.0%Claude Opus 4.8
GPQA Diamond (Epoch AI run)91.0%83.5%Claude Opus 4.8
ARC-AGI92.5%Not measuredOnly Claude Opus 4.8 measured
ARC-AGI-272.1%Not measuredOnly Claude Opus 4.8 measured
SimpleBench64.8%Not measuredOnly Claude Opus 4.8 measured
Chess puzzles52.0%Not measuredOnly Claude Opus 4.8 measured
Mystery game puzzles36.0%Not measuredOnly Claude Opus 4.8 measured
EBR-bench28.6%Not measuredOnly Claude Opus 4.8 measured
LMArena Text: hard prompts14821400Claude Opus 4.8
LMArena Text: expert15031408Claude Opus 4.8
LMArena Text: hard prompts in English14871411Claude Opus 4.8
LMArena Vision: diagrams1315Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: homework1337Not measuredOnly Claude Opus 4.8 measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-A3BLeader
OTIS Mock AIME98.3%70.0%Claude Opus 4.8
FrontierMath, tiers 1 to 380.0%Not measuredOnly Claude Opus 4.8 measured
FrontierMath, tier 456.1%Not measuredOnly Claude Opus 4.8 measured
ProofBench69.0%Not measuredOnly Claude Opus 4.8 measured
LMArena Text: math14861404Claude Opus 4.8
LMArena Text: mathematical work14971410Claude Opus 4.8
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 Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-A3BLeader
MMLU Pro89.6%Not measuredOnly Claude Opus 4.8 measured
LegalBench (Vals AI)83.6%Not measuredOnly Claude Opus 4.8 measured
CorpFin (Vals AI)66.7%Not measuredOnly Claude Opus 4.8 measured
TaxEval (Vals AI)75.6%Not measuredOnly Claude Opus 4.8 measured
MortgageTax (Vals AI)69.9%Not measuredOnly Claude Opus 4.8 measured
MedCode (Vals AI)53.2%Not measuredOnly Claude Opus 4.8 measured
SimpleQA Verified53.0%Not measuredOnly Claude Opus 4.8 measured
LMArena Text: science14771413Claude Opus 4.8
LMArena Text: medicine and healthcare14641421Claude Opus 4.8
LMArena Text: legal and government14771399Claude Opus 4.8
LMArena Text: business and finance14581394Claude Opus 4.8
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-A3BLeader
Design Arena: websites1258Not measuredOnly Claude Opus 4.8 measured
Design Arena: UI components1256Not measuredOnly Claude Opus 4.8 measured
Design Arena: game dev1264Not measuredOnly Claude Opus 4.8 measured
Design Arena: data viz1240Not measuredOnly Claude Opus 4.8 measured
Design Arena: 3D1223Not measuredOnly Claude Opus 4.8 measured
Design Arena: all code categories1255Not measuredOnly Claude Opus 4.8 measured
Design Arena: SVG1194Not measuredOnly Claude Opus 4.8 measured
Design Arena: ASCII art1276Not measuredOnly Claude Opus 4.8 measured
LiveBench design78.8%Not measuredOnly Claude Opus 4.8 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index201
General benchmarks for Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-A3BLeader
LiveBench data analysis66.0%Not measuredOnly Claude Opus 4.8 measured
Public Benefits Bench (Vals AI)68.1%Not measuredOnly Claude Opus 4.8 measured
SAGE (Vals AI)54.8%Not measuredOnly Claude Opus 4.8 measured
MedScribe (Vals AI)85.8%Not measuredOnly Claude Opus 4.8 measured
Vals Multimodal Index70.9%Not measuredOnly Claude Opus 4.8 measured
IFBench62.2%72.5%Qwen3.5-35B-A3B
Long context reasoning (AA-LCR)77.7%72.0%Claude Opus 4.8
DTBench94.9%80.0%Claude Opus 4.8
LMCA57.5%29.5%Claude Opus 4.8
Surface Evolver Bench87.5%Not measuredOnly Claude Opus 4.8 measured
Furniture assembly42.5%Not measuredOnly Claude Opus 4.8 measured
LMArena Text14611395Claude Opus 4.8
LMArena Text: instruction following14761379Claude Opus 4.8
LMArena Text: creative writing14541347Claude Opus 4.8
LMArena Vision1294Not measuredOnly Claude Opus 4.8 measured
LMArena Text: English14681407Claude Opus 4.8
LMArena Text: non-English14501378Claude Opus 4.8
LMArena Text: Chinese15061457Claude Opus 4.8
LMArena Text: German14721367Claude Opus 4.8
LMArena Text: Japanese14421326Claude Opus 4.8
LMArena Text: Korean14311355Claude Opus 4.8
LMArena Text: French14811412Claude Opus 4.8
LMArena Text: Spanish14651390Claude Opus 4.8
LMArena Text: Russian14741376Claude Opus 4.8
LMArena Text: Polish14591385Claude Opus 4.8
LMArena Text: multi-turn14761390Claude Opus 4.8
LMArena Text: longer queries14831389Claude Opus 4.8
LMArena Text: writing and language14611365Claude Opus 4.8
LMArena Text: entertainment and media14401347Claude Opus 4.8
LMArena Vision: English1293Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: Chinese1346Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: reading text1303Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: humor1282Not measuredOnly Claude Opus 4.8 measured
LMArena Vision: creative writing1301Not measuredOnly Claude Opus 4.8 measured
LMArena Document1475Not measuredOnly Claude Opus 4.8 measured
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 Claude Opus 4.8 and Qwen3.5-35B-A3B
BenchmarkClaude Opus 4.8Qwen3.5-35B-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

Claude Opus 4.8$0.018Fits in one request
Qwen3.5-35B-A3B$0.0006Fits in one request

Qwen3.5-35B-A3B costs less

Repository review

50K input + 3K output tokens

Claude Opus 4.8$0.325Fits in one request
Qwen3.5-35B-A3B$0.010Fits in one request

Qwen3.5-35B-A3B costs less

Agent loop

200K input + 10K output tokens

Claude Opus 4.8$1.25Fits in one request
Qwen3.5-35B-A3B$0.038Fits in one request

Qwen3.5-35B-A3B 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 Claude Opus 4.8 and Qwen3.5-35B-A3B
SpecificationAnthropic Claude Opus 4.8Qwen3.5-35B-A3B
MakerAnthropicQwen
WeightsNo public weights listedOpen weights
ReleasedMay 27, 2026February 25, 2026
Context window1M262K
Input $/Mtok$5.00$0.14
Output $/Mtok$25.00$1.00
Coding index66.07.7
Independent measurements behind it21
Cost per solved task$2.17Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.8 better than Qwen3.5-35B-A3B for coding?

Pick Claude Opus 4.8 for coding: it scores 66.0 on the Vibe Coding Index against 7.7 for Qwen3.5-35B-A3B. Pick Qwen3.5-35B-A3B when output tokens cost 25.0x less, or when the benchmark that matters is IFBench, where it leads.

Which is cheaper, Claude Opus 4.8 or Qwen3.5-35B-A3B?

Claude Opus 4.8 costs $5.00 per million input tokens and $25.00 per million output tokens; Qwen3.5-35B-A3B costs $0.14 and $1.00. Cost per solved task is known for Claude Opus 4.8 only.

Which has the bigger context window, Claude Opus 4.8 or Qwen3.5-35B-A3B?

Claude Opus 4.8, with 1M tokens against 262K.

How many benchmarks is the Claude Opus 4.8 vs Qwen3.5-35B-A3B comparison based on?

44 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.8's coding index rests on 2 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.

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