Qwen3 VL 8B Instruct
by Alibaba (Qwen) · qwen/qwen3-vl-8b-instruct · #64 cheapest of 325 paid models
Prices updated Jul 22, 2026, 1:34 AM UTC · refreshed hourly
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...
Compare vs:Claude Sonnet 5Claude Opus 4.6Claude Haiku 4.5GPT-5.6 Sol
Pricing
Input
$0.117
per 1M tokens
Output
$0.455
per 1M tokens
Blended (3:1)
$0.202
3 input : 1 output
Cache read
—
per 1M cached tokens
Cache write
—
per 1M tokens
Cache & batch economics
Effective prices for Qwen3 VL 8B Instruct (no cache-read rate published — hits billed as normal input).
| Cache hit rate | Effective blended $/1M | Example request* | vs no cache |
|---|---|---|---|
| 0% | $0.202 | $0.0124 | — |
| 50% | $0.202 | $0.0124 | −0% |
| 90% | $0.202 | $0.0124 | −0% |
*Example: 100K sticky context + 2K new input + 1K output. Batch discount is an approximate 50% off list rates (available for OpenAI / Anthropic / Google — not flagged for this creator). Confirm on the vendor’s pricing page.
What a request costs
| Workload | Input tokens | Output tokens | Cost / request | Cost / 1K requests |
|---|---|---|---|---|
| Short chat message | 500 | 300 | $0.000195 | $0.195 |
| Document summary | 8,000 | 1,000 | $0.001391 | $1.39 |
| Codebase question (RAG) | 30,000 | 2,000 | $0.00442 | $4.42 |
| Long-context analysis | 150,000 | 5,000 | $0.0198 | $19.82 |
Performance
Intelligence Index
8.4
AA composite quality
Coding Index
—
Math Index
27.3
Agentic Index
—
Output speed
140 tok/s
median
Time to first token
0.91s
median
Time to first answer
0.91s
after reasoning tokens
Artificial Analysis benchmarks
| Benchmark | Score |
|---|---|
| MMLU-Pro | 68.6% |
| GPQA Diamond | 42.7% |
| Humanity's Last Exam | 2.9% |
| LiveCodeBench | 33.2% |
| SciCode | 17.4% |
| AIME 2025 | 27.3% |
| IFBench | 32.3% |
| AA-LCR (Long Context Reasoning) | 15.3% |
| Terminal-Bench Hard | 2.3% |
| τ²-Bench (Telecom) | 29.2% |
Specs
Context window
262K
tokens
Max output
33K
tokens
Input modalities
image, text
Tokenizer
Qwen3