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GPT-4 vs Qwen3.5-35B-A3B

Updated October 21, 2026. 34 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-4 Replace Qwen3.5-35B-A3B
GPT-4OpenAI
Coding
22.4
Overall
6.7
Output $/M
$60.00
0
Qwen3.5-35B-A3BQwen
Coding
7.7
Overall
19.3
Output $/M
$1.00
34
vs boards won of 34 shared

Decision reading

Pick GPT-4 for coding: it scores 22.4 on the Vibe Coding Index against 7.7 for Qwen3.5-35B-A3B.

Pick Qwen3.5-35B-A3B when output tokens cost 60.0x less, or when the context window is larger, 262K against 8K tokens.

Model AGPT-4OpenAI22.4/1001 independent benchmarkNo cost per solved taskModel BQwen3.5-35B-A3BQwen7.7/1001 independent benchmarkNo cost per solved task
Shared results
34
GPT-4 only
11
Qwen3.5-35B-A3B only
11
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
GPT-4 22.4 from 1 benchmark, Qwen3.5-35B-A3B 7.7 from 1 benchmark.
Widest category
Knowledge, 40.4 points apart on average over 4 shared benchmarks.
Widest single gap
GPQA Diamond (Artificial Analysis run): GPT-4 34.9% against Qwen3.5-35B-A3B 84.5%, 49.6 points apart on a 0 to 100 scale.
Output price
GPT-4 $60.00 against Qwen3.5-35B-A3B $1.00 per million output tokens, 60.0x apart.
Context window
GPT-4 takes 8K 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
CategoryGPT-4BarsQwen3.5-35B-A3BTies
Coding02
Intelligence02
Reasoning04
Math02
Knowledge04
General020
All shared boards034
CodingLike-for-likeFour of these boards feed the Vibe Coding Index02
Coding benchmarks for GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-A3BLeader
SWE-bench Verified22.4%Not measuredOnly GPT-4 measured
SWE-bench Lite39.7%Not measuredOnly GPT-4 measured
SWE-bench (full test set)12.5%Not measuredOnly GPT-4 measured
LMArena WebDevNot measured1254Only Qwen3.5-35B-A3B measured
AA Coding Index13.1Not measuredOnly GPT-4 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured26.5%Only Qwen3.5-35B-A3B measured
ALE-BenchNot measured221.8Only Qwen3.5-35B-A3B measured
LMArena Text: coding12091410Qwen3.5-35B-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 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-A3BLeader
AA Intelligence Index6.719.3Qwen3.5-35B-A3B
Epoch Capabilities Index125.7142.5Qwen3.5-35B-A3B
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-A3BLeader
τ²-Bench TelecomNot measured89.2%Only Qwen3.5-35B-A3B measured
Vending-Bench 2Not measured$463Only Qwen3.5-35B-A3B measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-A3BLeader
GPQA Diamond (Artificial Analysis run)34.9%84.5%Qwen3.5-35B-A3B
Humanity's Last Exam (Artificial Analysis run)Not measured21.0%Only Qwen3.5-35B-A3B measured
GPQA Diamond (Epoch AI run)Not measured83.5%Only Qwen3.5-35B-A3B measured
BIG-Bench Hard75.1%Not measuredOnly GPT-4 measured
LMArena Text: hard prompts12001400Qwen3.5-35B-A3B
LMArena Text: expert11491408Qwen3.5-35B-A3B
LMArena Text: hard prompts in English12091411Qwen3.5-35B-A3B
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-A3BLeader
OTIS Mock AIMENot measured70.0%Only Qwen3.5-35B-A3B measured
GSM8K92.0%Not measuredOnly GPT-4 measured
LMArena Text: math12301404Qwen3.5-35B-A3B
LMArena Text: mathematical work12281410Qwen3.5-35B-A3B
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-A3BLeader
MMLU Pro (Artificial Analysis run)56.2%Not measuredOnly GPT-4 measured
MMLU86.4%Not measuredOnly GPT-4 measured
AI2 Reasoning Challenge96.3%Not measuredOnly GPT-4 measured
LMArena Text: science11771413Qwen3.5-35B-A3B
LMArena Text: medicine and healthcare11451421Qwen3.5-35B-A3B
LMArena Text: legal and government11911399Qwen3.5-35B-A3B
LMArena Text: business and finance11451394Qwen3.5-35B-A3B
Neither model has a result on the other 10 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-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 GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-A3BLeader
IFBench33.2%72.5%Qwen3.5-35B-A3B
Long context reasoning (AA-LCR)Not measured72.0%Only Qwen3.5-35B-A3B measured
DTBench62.7%80.0%Qwen3.5-35B-A3B
LMCA17.1%29.5%Qwen3.5-35B-A3B
HellaSwag95.3%Not measuredOnly GPT-4 measured
WinoGrande87.5%Not measuredOnly GPT-4 measured
LMArena Text12061395Qwen3.5-35B-A3B
LMArena Text: instruction following12011379Qwen3.5-35B-A3B
LMArena Text: creative writing11911347Qwen3.5-35B-A3B
LMArena Text: English12201407Qwen3.5-35B-A3B
LMArena Text: non-English11941378Qwen3.5-35B-A3B
LMArena Text: Chinese11841457Qwen3.5-35B-A3B
LMArena Text: German11981367Qwen3.5-35B-A3B
LMArena Text: Japanese11371326Qwen3.5-35B-A3B
LMArena Text: Korean10871355Qwen3.5-35B-A3B
LMArena Text: French12201412Qwen3.5-35B-A3B
LMArena Text: Spanish11971390Qwen3.5-35B-A3B
LMArena Text: Russian11951376Qwen3.5-35B-A3B
LMArena Text: Polish12651385Qwen3.5-35B-A3B
LMArena Text: multi-turn12061390Qwen3.5-35B-A3B
LMArena Text: longer queries11901389Qwen3.5-35B-A3B
LMArena Text: writing and language12191365Qwen3.5-35B-A3B
LMArena Text: entertainment and media11841347Qwen3.5-35B-A3B
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4 and Qwen3.5-35B-A3B
BenchmarkGPT-4Qwen3.5-35B-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

GPT-4$0.060Fits 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

GPT-4$1.68Exceeds the context window
Qwen3.5-35B-A3B$0.010Fits in one request

Qwen3.5-35B-A3B costs less

Agent loop

200K input + 10K output tokens

GPT-4$6.60Exceeds the context window
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 GPT-4 and Qwen3.5-35B-A3B
SpecificationOpenAI GPT-4Qwen3.5-35B-A3B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedMay 28, 2023February 25, 2026
Context window8K262K
Input $/Mtok$30.00$0.14
Output $/Mtok$60.00$1.00
Coding index22.47.7
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4 better than Qwen3.5-35B-A3B for coding?

Pick GPT-4 for coding: it scores 22.4 on the Vibe Coding Index against 7.7 for Qwen3.5-35B-A3B. Pick Qwen3.5-35B-A3B when output tokens cost 60.0x less, or when the context window is larger, 262K against 8K tokens.

Which is cheaper, GPT-4 or Qwen3.5-35B-A3B?

GPT-4 costs $30.00 per million input tokens and $60.00 per million output tokens; Qwen3.5-35B-A3B costs $0.14 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4 or Qwen3.5-35B-A3B?

Qwen3.5-35B-A3B, with 262K tokens against 8K.

How many benchmarks is the GPT-4 vs Qwen3.5-35B-A3B comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. GPT-4's coding index rests on 1 independent benchmark 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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