GPT-3.5 Turbo (older v0613) vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 10.0x less per output token than GPT-3.5 Turbo (older v0613) ($0.20 vs $2.00 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GPT-3.5 Turbo (older v0613) or Qwen2.5 7B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo (older v0613) for work measured by MMLU, where it leads.
- Shared results
- 1
- GPT-3.5 Turbo (older v0613) only
- 2
- Qwen2.5 7B Instruct only
- 6
- 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
Long documents
Listed context window
Qwen2.5 7B Instruct
Qwen2.5 7B Instruct takes 33K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
GPT-3.5 Turbo (older v0613) $0.0020, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo (older v0613) not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- Knowledge, 4.0 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU: GPT-3.5 Turbo (older v0613) 68.9% against Qwen2.5 7B Instruct 72.9%, 4.0 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo (older v0613) $2.00 against Qwen2.5 7B Instruct $0.20 per million output tokens, 10.0x apart.
- Context window
- GPT-3.5 Turbo (older v0613) takes 4K tokens, Qwen2.5 7B Instruct 33K.
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 (older v0613) | Bars | Qwen2.5 7B Instruct | Ties |
|---|---|---|---|---|
| Knowledge | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingNot comparableSix of these boards feed the Vibe Coding Index0 boards, none shared
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 43 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 118.5 | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Epoch AI run) | Not measured | 35.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 22 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 2.5% | Only Qwen2.5 7B Instruct measured |
| GSM8K | 46.9% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU | 68.9% | 72.9% | Qwen2.5 7B Instruct |
| Neither model has a result on the other 19 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| DTBench | Not measured | 47.7% | Only Qwen2.5 7B Instruct measured |
| LMCA | Not measured | 6.4% | Only Qwen2.5 7B Instruct measured |
| WinoGrande | 81.6% | Not measured | Only GPT-3.5 Turbo (older v0613) measured |
| Neither model has a result on the other 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo (older v0613) | Qwen2.5 7B Instruct | 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
Qwen2.5 7B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 7B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 7B Instruct 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 (older v0613) | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | January 25, 2024 | October 16, 2024 |
| Context window | 4K | 33K |
| Input $/Mtok | $1.00 | $0.10 |
| Output $/Mtok | $2.00 | $0.20 |
| 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 (older v0613) better than Qwen2.5 7B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo (older v0613) for work measured by MMLU, where it leads.
Which is cheaper, GPT-3.5 Turbo (older v0613) or Qwen2.5 7B Instruct?
GPT-3.5 Turbo (older v0613) costs $1.00 per million input tokens and $2.00 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo (older v0613) or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct, with 33K tokens against 4K.
How many benchmarks is the GPT-3.5 Turbo (older v0613) vs Qwen2.5 7B Instruct comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. GPT-3.5 Turbo (older v0613)'s coding index rests on 0 independent benchmarks and Qwen2.5 7B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.