GPT-3.5 Turbo Instruct vs Qwen3.8 Flash
Qwen3.8 Flash costs 4.3x less per output token than GPT-3.5 Turbo Instruct ($0.47 vs $2.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.47
GPT-3.5 Turbo Instruct 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 Instruct has results on METR time horizons; 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
Long documents
Listed context window
Qwen3.8 Flash
Qwen3.8 Flash takes 1M tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Flash
GPT-3.5 Turbo Instruct $0.0025, Qwen3.8 Flash $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo Instruct 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 Instruct $2.00 against Qwen3.8 Flash $0.47 per million output tokens, 4.3x apart.
- Context window
- GPT-3.5 Turbo Instruct takes 4K 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 Index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 87.4% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 38 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | 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 |
| METR time horizons | 21.5% | Not measured | Only GPT-3.5 Turbo Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 87.4% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 21 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | 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 index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 74.2% | Only Qwen3.8 Flash 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 Instruct | Qwen3.8 Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 28, 2023 | August 26, 2026 |
| Context window | 4K | 1M |
| Input $/Mtok | $1.50 | $0.15 |
| Output $/Mtok | $2.00 | $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 Instruct 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 Instruct or Qwen3.8 Flash?
GPT-3.5 Turbo Instruct costs $1.50 per million input tokens and $2.00 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 Instruct or Qwen3.8 Flash?
Qwen3.8 Flash, with 1M tokens against 4K.
Has any benchmark tested both GPT-3.5 Turbo Instruct and Qwen3.8 Flash?
No benchmark has tested both yet. GPT-3.5 Turbo Instruct has results on METR time horizons; Qwen3.8 Flash on LiveBench coding, LiveBench research and LiveBench browser. Compare them on price and context instead. Data as of October 22, 2026.