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GPT-3.5 Turbo vs Qwen3.6 35B A3B

Qwen3.6 35B A3B leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 18.2 to 5.5, and Qwen3.6 35B A3B costs 1.5x less per output token ($1.00 vs $1.50 per million). Updated October 22, 2026; 5 benchmarks have a result for both.

Replace GPT-3.5 Turbo Replace Qwen3.6 35B A3B
GPT-3.5 TurboOpenAI
Coding
No data
Overall
No data
Output $/M
$1.50
0
Qwen3.6 35B A3BQwen
Coding
No data
Overall
45.0
Output $/M
$1.00
5
vs boards won of 5 shared

GPT-3.5 Turbo or Qwen3.6 35B A3B: which to pick

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

Pick GPT-3.5 Turbo for work measured by AA Coding Index or AA Intelligence Index, where it leads.

Model AGPT-3.5 TurboOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.6 35B A3BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
5
GPT-3.5 Turbo only
8
Qwen3.6 35B A3B only
15
Like-for-like categories
4 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
GPT-3.5 Turbo not benchmarked, Qwen3.6 35B A3B not benchmarked.
Widest category
Reasoning, 54.4 points apart on average over 1 shared benchmark.
Widest single gap
GPQA Diamond (Artificial Analysis run): GPT-3.5 Turbo 29.7% against Qwen3.6 35B A3B 84.1%, 54.4 points apart on a 0 to 100 scale.
Output price
GPT-3.5 Turbo $1.50 against Qwen3.6 35B A3B $1.00 per million output tokens, 1.5x apart.
Context window
GPT-3.5 Turbo takes 16K tokens, Qwen3.6 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-3.5 TurboBarsQwen3.6 35B A3BTies
Coding01
Intelligence01
Reasoning01
General02
All shared boards05
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 35B A3BLeader
AA Coding Index10.741.9Qwen3.6 35B A3B
SciCodeNot measured36.6%Only Qwen3.6 35B A3B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured34.8%Only Qwen3.6 35B A3B measured
Terminal-BenchNot measured24.6%Only Qwen3.6 35B A3B measured
WeirdMLNot measured34.5%Only Qwen3.6 35B A3B measured
SciCode0.0%Not measuredOnly GPT-3.5 Turbo measured
Neither model has a result on the other 37 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 35B A3BLeader
AA Intelligence Index5.518.2Qwen3.6 35B A3B
Epoch Capabilities IndexNot measured143.7Only Qwen3.6 35B A3B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 35B A3BLeader
AA Agentic IndexNot measured13.1Only Qwen3.6 35B A3B measured
τ²-Bench TelecomNot measured95.3%Only Qwen3.6 35B A3B measured
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
Reasoning benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 35B A3BLeader
GPQA Diamond30.6%Not measuredOnly GPT-3.5 Turbo measured
GPQA Diamond (Artificial Analysis run)29.7%84.1%Qwen3.6 35B A3B
Humanity's Last Exam (Artificial Analysis run)Not measured22.2%Only Qwen3.6 35B A3B measured
GPQA Diamond (Epoch AI run)Not measured84.8%Only Qwen3.6 35B A3B measured
Mystery game puzzlesNot measured22.0%Only Qwen3.6 35B A3B measured
BIG-Bench Hard61.6%Not measuredOnly GPT-3.5 Turbo measured
Neither model has a result on the other 17 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Math benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 35B A3BLeader
OTIS Mock AIMENot measured86.7%Only Qwen3.6 35B A3B measured
FrontierMath, tiers 1 to 3Not measured20.4%Only Qwen3.6 35B A3B measured
GSM8K57.8%Not measuredOnly GPT-3.5 Turbo measured
Neither model has a result on the other 11 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Knowledge benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 35B A3BLeader
LegalBench (Vals AI)64.4%Not measuredOnly GPT-3.5 Turbo measured
MedQA (Vals AI)58.5%Not measuredOnly GPT-3.5 Turbo measured
MMLU Pro (Artificial Analysis run)46.2%Not measuredOnly GPT-3.5 Turbo measured
MMLU68.5%Not measuredOnly GPT-3.5 Turbo measured
Neither model has a result on the other 16 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 35B A3BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 35B A3BLeader
IFBenchNot measured64.4%Only Qwen3.6 35B A3B measured
Long context reasoning (AA-LCR)Not measured71.7%Only Qwen3.6 35B A3B measured
DTBench48.5%73.9%Qwen3.6 35B A3B
LMCA9.7%29.7%Qwen3.6 35B A3B
Surface Evolver BenchNot measured44.4%Only Qwen3.6 35B A3B measured
Neither model has a result on the other 43 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-3.5 Turbo and Qwen3.6 35B A3B
BenchmarkGPT-3.5 TurboQwen3.6 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-3.5 Turbo$0.0013Fits in one request
Qwen3.6 35B A3B$0.0006Fits in one request

Qwen3.6 35B A3B costs less

Repository review

50K input + 3K output tokens

GPT-3.5 Turbo$0.029Exceeds the context window
Qwen3.6 35B A3B$0.0080Fits in one request

Qwen3.6 35B A3B costs less

Agent loop

200K input + 10K output tokens

GPT-3.5 Turbo$0.115Exceeds the context window
Qwen3.6 35B A3B$0.030Fits in one request

Qwen3.6 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-3.5 Turbo and Qwen3.6 35B A3B
SpecificationOpenAI GPT-3.5 TurboQwen3.6 35B A3B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedMay 28, 2023April 27, 2026
Context window16K262K
Input $/Mtok$0.50$0.10
Output $/Mtok$1.50$1.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-3.5 Turbo better than Qwen3.6 35B A3B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo for work measured by AA Coding Index or AA Intelligence Index, where it leads.

Which is cheaper, GPT-3.5 Turbo or Qwen3.6 35B A3B?

GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-3.5 Turbo or Qwen3.6 35B A3B?

Qwen3.6 35B A3B, with 262K tokens against 16K.

How many benchmarks is the GPT-3.5 Turbo vs Qwen3.6 35B A3B comparison based on?

5 benchmarks have a result for both models as of October 22, 2026. GPT-3.5 Turbo's coding index rests on 0 independent benchmarks and Qwen3.6 35B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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