GPT-3.5 Turbo 16k vs Qwen3.7 Flash
Qwen3.7 Flash costs 30.8x less per output token than GPT-3.5 Turbo 16k ($0.13 vs $4.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.13
GPT-3.5 Turbo 16k or Qwen3.7 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-3.5 Turbo 16k has none yet; Qwen3.7 Flash on ALE-Bench, Epoch Capabilities Index and GPQA Diamond (Epoch AI run). Compare them on price and context instead.
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
Long documents
Listed context window
Qwen3.7 Flash
Qwen3.7 Flash takes 1M tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Flash
GPT-3.5 Turbo 16k $0.0050, Qwen3.7 Flash $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo 16k not benchmarked, Qwen3.7 Flash not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-3.5 Turbo 16k $4.00 against Qwen3.7 Flash $0.13 per million output tokens, 30.8x apart.
- Context window
- GPT-3.5 Turbo 16k takes 16K tokens, Qwen3.7 Flash 1M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board measured
| Benchmark | GPT-3.5 Turbo 16k | Qwen3.7 Flash | Leader |
|---|---|---|---|
| ALE-Bench | Not measured | 420.1 | Only Qwen3.7 Flash measured |
| Neither model has a result on the other 42 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo 16k | Qwen3.7 Flash | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 144.1 | Only Qwen3.7 Flash measured |
| Neither model has a result on the other 1 board here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Qwen3.7 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.7 Flash measured |
| Mystery game puzzles | Not measured | 15.0% | Only Qwen3.7 Flash measured |
| Neither model has a result on the other 21 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-3.5 Turbo 16k | Qwen3.7 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 86.7% | Only Qwen3.7 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 19.3% | Only Qwen3.7 Flash measured |
| Neither model has a result on the other 12 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
Qwen3.7 Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 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 16k | Qwen3.7 Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | August 28, 2023 | July 27, 2026 |
| Context window | 16K | 1M |
| Input $/Mtok | $3.00 | $0.03 |
| Output $/Mtok | $4.00 | $0.13 |
| 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 16k better than Qwen3.7 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-3.5 Turbo 16k or Qwen3.7 Flash?
GPT-3.5 Turbo 16k costs $3.00 per million input tokens and $4.00 per million output tokens; Qwen3.7 Flash costs $0.03 and $0.13. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo 16k or Qwen3.7 Flash?
Qwen3.7 Flash, with 1M tokens against 16K.
Has any benchmark tested both GPT-3.5 Turbo 16k and Qwen3.7 Flash?
No benchmark has tested both yet. GPT-3.5 Turbo 16k has none yet; Qwen3.7 Flash on ALE-Bench, Epoch Capabilities Index and GPQA Diamond (Epoch AI run). Compare them on price and context instead. Data as of October 22, 2026.