GPT-3.5 Turbo vs Qwen2.5 72B Instruct
Qwen2.5 72B Instruct leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 7.7 to 5.5, and Qwen2.5 72B Instruct costs 3.8x less per output token ($0.40 vs $1.50 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GPT-3.5 Turbo or Qwen2.5 72B Instruct: 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 AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 7
- GPT-3.5 Turbo only
- 6
- Qwen2.5 72B Instruct only
- 46
- Like-for-like categories
- 4 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
Qwen2.5 72B Instruct
Qwen2.5 72B Instruct leads, 7.7 against 5.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
Qwen2.5 72B Instruct
Qwen2.5 72B Instruct takes 33K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 72B Instruct
GPT-3.5 Turbo $0.0013, Qwen2.5 72B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, Qwen2.5 72B Instruct not benchmarked.
- Widest category
- Knowledge, 21.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): GPT-3.5 Turbo 46.2% against Qwen2.5 72B Instruct 72.0%, 25.8 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against Qwen2.5 72B Instruct $0.40 per million output tokens, 3.8x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, Qwen2.5 72B 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 | Bars | Qwen2.5 72B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 7 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 27.6% | Only Qwen2.5 72B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 4.5% | Only Qwen2.5 72B Instruct measured |
| WeirdML | Not measured | 16.0% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: coding | Not measured | 1292 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: software and IT | Not measured | 1292 | Only Qwen2.5 72B Instruct measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 7.7 | Qwen2.5 72B Instruct |
| Epoch Capabilities Index | Not measured | 129.0 | Only Qwen2.5 72B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 34.5% | Only Qwen2.5 72B Instruct measured |
| METR time horizons | Not measured | 35.8% | Only Qwen2.5 72B Instruct measured |
| BALROG | Not measured | 16.2% | Only Qwen2.5 72B Instruct measured |
| TheAgentCompany | Not measured | 5.7% | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | 49.1% | Qwen2.5 72B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.6% | Only Qwen2.5 72B Instruct measured |
| GPQA Diamond (Epoch AI run) | Not measured | 49.1% | Only Qwen2.5 72B Instruct measured |
| BIG-Bench Hard | 61.6% | 79.8% | Qwen2.5 72B Instruct |
| LMArena Text: hard prompts | Not measured | 1271 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: expert | Not measured | 1245 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1279 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 14.0 | Only Qwen2.5 72B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 14.0% | Only Qwen2.5 72B Instruct measured |
| OTIS Mock AIME | Not measured | 8.1% | Only Qwen2.5 72B Instruct measured |
| MATH level 5 | Not measured | 63.2% | Only Qwen2.5 72B Instruct measured |
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text: math | Not measured | 1283 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1290 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B Instruct | 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% | 72.0% | Qwen2.5 72B Instruct |
| MMLU | 68.5% | 85.3% | Qwen2.5 72B Instruct |
| AI2 Reasoning Challenge | Not measured | 94.5% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: science | Not measured | 1275 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1258 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: legal and government | Not measured | 1288 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: business and finance | Not measured | 1253 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 36.9% | Only Qwen2.5 72B Instruct measured |
| DTBench | 48.5% | 62.9% | Qwen2.5 72B Instruct |
| LMCA | 9.7% | 13.4% | Qwen2.5 72B Instruct |
| HellaSwag | Not measured | 84.8% | Only Qwen2.5 72B Instruct measured |
| WinoGrande | Not measured | 82.3% | Only Qwen2.5 72B Instruct measured |
| PIQA | Not measured | 82.6% | Only Qwen2.5 72B Instruct measured |
| LMArena Text | Not measured | 1269 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: instruction following | Not measured | 1254 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: creative writing | Not measured | 1221 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: English | Not measured | 1283 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: non-English | Not measured | 1252 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Chinese | Not measured | 1273 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: German | Not measured | 1234 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Japanese | Not measured | 1180 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Korean | Not measured | 1188 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: French | Not measured | 1280 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Spanish | Not measured | 1255 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Russian | Not measured | 1264 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1272 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: longer queries | Not measured | 1281 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: writing and language | Not measured | 1247 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1211 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | Qwen2.5 72B 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 72B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 72B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 72B 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 | Qwen2.5 72B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | September 19, 2024 |
| Context window | 16K | 33K |
| Input $/Mtok | $0.50 | $0.36 |
| Output $/Mtok | $1.50 | $0.40 |
| 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 Qwen2.5 72B Instruct 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 AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GPT-3.5 Turbo or Qwen2.5 72B Instruct?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; Qwen2.5 72B Instruct costs $0.36 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct, with 33K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs Qwen2.5 72B Instruct comparison based on?
7 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 Qwen2.5 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.