GPT-3.5 Turbo vs Qwen3.8 Flash
Qwen3.8 Flash costs 3.2x less per output token than GPT-3.5 Turbo ($0.47 vs $1.50 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.47
GPT-3.5 Turbo or Qwen3.8 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 has results on AA Coding Index, SciCode and AA Intelligence Index; Qwen3.8 Flash on LiveBench coding, LiveBench research and LiveBench browser. 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
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
Qwen3.8 Flash
Qwen3.8 Flash takes 1M tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Flash
GPT-3.5 Turbo $0.0013, Qwen3.8 Flash $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, Qwen3.8 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 $1.50 against Qwen3.8 Flash $0.47 per million output tokens, 3.2x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, Qwen3.8 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 Index3 boards measured
| Benchmark | GPT-3.5 Turbo | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 87.4% | Only Qwen3.8 Flash measured |
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo 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 index1 board measured
| Benchmark | GPT-3.5 Turbo | Qwen3.8 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-3.5 Turbo | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 74.6% | Only Qwen3.8 Flash measured |
| LiveBench browser | Not measured | 48.0% | Only Qwen3.8 Flash measured |
| LiveBench long agentic | Not measured | 37.1% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-3.5 Turbo | Qwen3.8 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| LiveBench reasoning | Not measured | 87.4% | Only Qwen3.8 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | Not measured | Only GPT-3.5 Turbo measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo | Qwen3.8 Flash | Leader |
|---|---|---|---|
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-3.5 Turbo | Qwen3.8 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 |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench design | Not measured | 72.8% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-3.5 Turbo | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 74.2% | Only Qwen3.8 Flash measured |
| DTBench | 48.5% | Not measured | Only GPT-3.5 Turbo measured |
| LMCA | 9.7% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 45 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
Qwen3.8 Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.8 Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 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 | Qwen3.8 Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | August 26, 2026 |
| Context window | 16K | 1M |
| Input $/Mtok | $0.50 | $0.15 |
| Output $/Mtok | $1.50 | $0.47 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo better than Qwen3.8 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 or Qwen3.8 Flash?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; Qwen3.8 Flash costs $0.15 and $0.47. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or Qwen3.8 Flash?
Qwen3.8 Flash, with 1M tokens against 16K.
Has any benchmark tested both GPT-3.5 Turbo and Qwen3.8 Flash?
No benchmark has tested both yet. GPT-3.5 Turbo has results on AA Coding Index, SciCode and AA Intelligence Index; Qwen3.8 Flash on LiveBench coding, LiveBench research and LiveBench browser. Compare them on price and context instead. Data as of October 22, 2026.