GPT-4 vs Qwen3 VL 8B Instruct
Qwen3 VL 8B Instruct leads GPT-4 on the Artificial Analysis Intelligence Index, 7.3 to 6.7, and Qwen3 VL 8B Instruct costs 80.0x less per output token ($0.75 vs $60.00 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
GPT-4 or Qwen3 VL 8B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4 for work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 4
- GPT-4 only
- 41
- Qwen3 VL 8B Instruct only
- 7
- 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
Qwen3 VL 8B Instruct
Qwen3 VL 8B Instruct leads, 7.3 against 6.7.
Same board, both measured
Long documents
Listed context window
Qwen3 VL 8B Instruct
Qwen3 VL 8B Instruct takes 262K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 8B Instruct
GPT-4 $0.060, Qwen3 VL 8B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- GPT-4 not benchmarked, Qwen3 VL 8B Instruct not benchmarked.
- Widest category
- Reasoning, 23.0 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-4 34.9% against Qwen3 VL 8B Instruct 57.9%, 23.0 points apart on a 0 to 100 scale.
- Output price
- GPT-4 $60.00 against Qwen3 VL 8B Instruct $0.75 per million output tokens, 80.0x apart.
- Context window
- GPT-4 takes 8K tokens, Qwen3 VL 8B Instruct 262K.
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-4 | Bars | Qwen3 VL 8B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | GPT-4 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | 22.4% | Not measured | Only GPT-4 measured |
| SWE-bench Lite | 39.7% | Not measured | Only GPT-4 measured |
| SWE-bench (full test set) | 12.5% | Not measured | Only GPT-4 measured |
| AA Coding Index | 13.1 | Not measured | Only GPT-4 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 35.3% | Only Qwen3 VL 8B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.8% | Only Qwen3 VL 8B Instruct measured |
| LMArena Text: coding | 1209 | Not measured | Only GPT-4 measured |
| LMArena Text: software and IT | 1195 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 7.3 | Qwen3 VL 8B Instruct |
| Epoch Capabilities Index | 125.7 | Not measured | Only GPT-4 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 29.2% | Only Qwen3 VL 8B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 34.9% | 57.9% | Qwen3 VL 8B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.8% | Only Qwen3 VL 8B Instruct measured |
| BIG-Bench Hard | 75.1% | Not measured | Only GPT-4 measured |
| LMArena Text: hard prompts | 1200 | Not measured | Only GPT-4 measured |
| LMArena Text: expert | 1149 | Not measured | Only GPT-4 measured |
| LMArena Text: hard prompts in English | 1209 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 30.7 | Only Qwen3 VL 8B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 30.7% | Only Qwen3 VL 8B Instruct measured |
| GSM8K | 92.0% | Not measured | Only GPT-4 measured |
| LMArena Text: math | 1230 | Not measured | Only GPT-4 measured |
| LMArena Text: mathematical work | 1228 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 56.2% | 74.9% | Qwen3 VL 8B Instruct |
| MMLU | 86.4% | Not measured | Only GPT-4 measured |
| AI2 Reasoning Challenge | 96.3% | Not measured | Only GPT-4 measured |
| LMArena Text: science | 1177 | Not measured | Only GPT-4 measured |
| LMArena Text: medicine and healthcare | 1145 | Not measured | Only GPT-4 measured |
| LMArena Text: legal and government | 1191 | Not measured | Only GPT-4 measured |
| LMArena Text: business and finance | 1145 | Not measured | Only GPT-4 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-4 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| IFBench | 33.2% | 39.9% | Qwen3 VL 8B Instruct |
| Long context reasoning (AA-LCR) | Not measured | 33.3% | Only Qwen3 VL 8B Instruct measured |
| DTBench | 62.7% | Not measured | Only GPT-4 measured |
| LMCA | 17.1% | Not measured | Only GPT-4 measured |
| HellaSwag | 95.3% | Not measured | Only GPT-4 measured |
| WinoGrande | 87.5% | Not measured | Only GPT-4 measured |
| LMArena Text | 1206 | Not measured | Only GPT-4 measured |
| LMArena Text: instruction following | 1201 | Not measured | Only GPT-4 measured |
| LMArena Text: creative writing | 1191 | Not measured | Only GPT-4 measured |
| LMArena Text: English | 1220 | Not measured | Only GPT-4 measured |
| LMArena Text: non-English | 1194 | Not measured | Only GPT-4 measured |
| LMArena Text: Chinese | 1184 | Not measured | Only GPT-4 measured |
| LMArena Text: German | 1198 | Not measured | Only GPT-4 measured |
| LMArena Text: Japanese | 1137 | Not measured | Only GPT-4 measured |
| LMArena Text: Korean | 1087 | Not measured | Only GPT-4 measured |
| LMArena Text: French | 1220 | Not measured | Only GPT-4 measured |
| LMArena Text: Spanish | 1197 | Not measured | Only GPT-4 measured |
| LMArena Text: Russian | 1195 | Not measured | Only GPT-4 measured |
| LMArena Text: Polish | 1265 | Not measured | Only GPT-4 measured |
| LMArena Text: multi-turn | 1206 | Not measured | Only GPT-4 measured |
| LMArena Text: longer queries | 1190 | Not measured | Only GPT-4 measured |
| LMArena Text: writing and language | 1219 | Not measured | Only GPT-4 measured |
| LMArena Text: entertainment and media | 1184 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 | Qwen3 VL 8B 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
Qwen3 VL 8B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 VL 8B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 VL 8B 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-4 | Qwen3 VL 8B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | October 14, 2025 |
| Context window | 8K | 262K |
| Input $/Mtok | $30.00 | $0.25 |
| Output $/Mtok | $60.00 | $0.75 |
| 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-4 better than Qwen3 VL 8B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4 for work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GPT-4 or Qwen3 VL 8B Instruct?
GPT-4 costs $30.00 per million input tokens and $60.00 per million output tokens; Qwen3 VL 8B Instruct costs $0.25 and $0.75. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4 or Qwen3 VL 8B Instruct?
Qwen3 VL 8B Instruct, with 262K tokens against 8K.
How many benchmarks is the GPT-4 vs Qwen3 VL 8B Instruct comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. GPT-4's coding index rests on 0 independent benchmarks and Qwen3 VL 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.