GPT-3.5 Turbo vs Qwen3.6 Flash
Qwen3.6 Flash costs 1.3x less per output token than GPT-3.5 Turbo ($1.125 vs $1.50 per million). Updated October 22, 2026; 2 benchmarks have a result for both.
GPT-3.5 Turbo or Qwen3.6 Flash: 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 DTBench or LMCA, where it leads.
- Shared results
- 2
- GPT-3.5 Turbo only
- 11
- Qwen3.6 Flash only
- 9
- Like-for-like categories
- 1 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-3.5 Turbo has a AA Intelligence Index result, 5.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
Qwen3.6 Flash
Qwen3.6 Flash takes 1M tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 Flash
GPT-3.5 Turbo $0.0013, Qwen3.6 Flash $0.0008 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, Qwen3.6 Flash not benchmarked.
- Widest category
- General, 24.9 points apart on average over 2 shared benchmarks.
- Widest single gap
- DTBench: GPT-3.5 Turbo 48.5% against Qwen3.6 Flash 77.1%, 28.5 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against Qwen3.6 Flash $1.125 per million output tokens, 1.3x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, Qwen3.6 Flash 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.
| Category | GPT-3.5 Turbo | Bars | Qwen3.6 Flash | Ties |
|---|---|---|---|---|
| General | 0 | 2 | ||
| All shared boards | 0 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen3.6 Flash | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| ALE-Bench | Not measured | 326.4 | Only Qwen3.6 Flash measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 40 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen3.6 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | Not measured | Only GPT-3.5 Turbo measured |
| Epoch Capabilities Index | Not measured | 142.9 | Only Qwen3.6 Flash measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen3.6 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 42 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen3.6 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Epoch AI run) | Not measured | 83.3% | Only Qwen3.6 Flash measured |
| SimpleBench | Not measured | 35.2% | Only Qwen3.6 Flash measured |
| Chess puzzles | Not measured | 12.0% | Only Qwen3.6 Flash measured |
| Mystery game puzzles | Not measured | 18.0% | Only Qwen3.6 Flash measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen3.6 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.6 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 22.5% | Only Qwen3.6 Flash measured |
| GSM8K | 57.8% | Not measured | Only 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 index5 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen3.6 Flash | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 64.4% | Not measured | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | 58.5% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 46.2% | Not measured | Only GPT-3.5 Turbo measured |
| SimpleQA Verified | Not measured | 15.9% | Only Qwen3.6 Flash measured |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen3.6 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen3.6 Flash | Leader |
|---|---|---|---|
| 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
Qwen3.6 Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.6 Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 Flash 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.
| Specification | OpenAI GPT-3.5 Turbo | Qwen3.6 Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | April 27, 2026 |
| Context window | 16K | 1M |
| Input $/Mtok | $0.50 | $0.1875 |
| Output $/Mtok | $1.50 | $1.125 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo better than Qwen3.6 Flash 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 DTBench or LMCA, where it leads.
Which is cheaper, GPT-3.5 Turbo or Qwen3.6 Flash?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; Qwen3.6 Flash costs $0.1875 and $1.125. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or Qwen3.6 Flash?
Qwen3.6 Flash, with 1M tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs Qwen3.6 Flash comparison based on?
2 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 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.