Qwen3 VL 8B Instruct vs Step 3.7 Flash
Updated October 21, 2026. 7 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Qwen3 VL 8B Instruct has the edge where output tokens cost 1.5x less, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 7
- Qwen3 VL 8B Instruct only
- 4
- Step 3.7 Flash only
- 11
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Step 3.7 Flash
Step 3.7 Flash leads, 19.5 against 7.3.
Same board, both measured
Web design
Design Arena: websites
No call
Only Step 3.7 Flash has a Design Arena: websites result, 1196.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 8B Instruct
Qwen3 VL 8B Instruct $0.0006, Step 3.7 Flash $0.0008 per turn.
List prices
At a glance
- Coding index
- Qwen3 VL 8B Instruct not benchmarked, Step 3.7 Flash not benchmarked.
- Widest category
- Agentic work, 69.3 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Qwen3 VL 8B Instruct 29.2% against Step 3.7 Flash 98.5%, 69.3 points apart on a 0 to 100 scale.
- Output price
- Qwen3 VL 8B Instruct $0.75 against Step 3.7 Flash $1.15 per million output tokens, 1.5x apart.
- Context window
- Both take 262K tokens.
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 | Qwen3 VL 8B Instruct | Bars | Step 3.7 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 39.6 | Only Step 3.7 Flash measured |
| LiveCodeBench (Artificial Analysis run) | 35.3% | Not measured | Only Qwen3 VL 8B Instruct measured |
| SciCode | Not measured | 43.9% | Only Step 3.7 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | 35.6% | Step 3.7 Flash |
| ALE-Bench | Not measured | 694.1 | Only Step 3.7 Flash measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | 19.5 | Step 3.7 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 29.2% | 98.5% | Step 3.7 Flash |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 57.9% | 80.9% | Step 3.7 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 21.4% | Step 3.7 Flash |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | Leader |
|---|---|---|---|
| AA Math Index | 30.7 | Not measured | Only Qwen3 VL 8B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 30.7% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.9% | Not measured | Only Qwen3 VL 8B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1196 | Only Step 3.7 Flash measured |
| Design Arena: UI components | Not measured | 1179 | Only Step 3.7 Flash measured |
| Design Arena: game dev | Not measured | 1158 | Only Step 3.7 Flash measured |
| Design Arena: data viz | Not measured | 1172 | Only Step 3.7 Flash measured |
| Design Arena: 3D | Not measured | 1129 | Only Step 3.7 Flash measured |
| Design Arena: all code categories | Not measured | 1182 | Only Step 3.7 Flash measured |
| Design Arena: SVG | Not measured | 1085 | Only Step 3.7 Flash measured |
| Design Arena: ASCII art | Not measured | 1152 | Only Step 3.7 Flash measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | Leader |
|---|---|---|---|
| IFBench | 39.9% | 67.3% | Step 3.7 Flash |
| Long context reasoning (AA-LCR) | 33.3% | 73.7% | Step 3.7 Flash |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 VL 8B Instruct | Step 3.7 Flash | 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
Step 3.7 Flash costs less
Agent loop
200K input + 10K output tokens
Step 3.7 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 | Qwen3 VL 8B Instruct | StepFun Step 3.7 Flash |
|---|---|---|
| Maker | Qwen | StepFun |
| Weights | Open weights | Open weights |
| Released | October 14, 2025 | May 28, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.25 | $0.20 |
| Output $/Mtok | $0.75 | $1.15 |
| 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 Qwen3 VL 8B Instruct better than Step 3.7 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Qwen3 VL 8B Instruct has the edge where output tokens cost 1.5x less, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Qwen3 VL 8B Instruct or Step 3.7 Flash?
Qwen3 VL 8B Instruct costs $0.25 per million input tokens and $0.75 per million output tokens; Step 3.7 Flash costs $0.20 and $1.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3 VL 8B Instruct or Step 3.7 Flash?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3 VL 8B Instruct vs Step 3.7 Flash comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. Qwen3 VL 8B Instruct's coding index rests on 0 independent benchmarks and Step 3.7 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.