AI model comparison
Qwen3 VL 8B Instruct vs GPT-4o-mini
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
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.
- Input / 1M
- $0.25
- Output / 1M
- $0.75
- Context
- 262K
OpenAI
GPT-4o-mini
GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs.
- Input / 1M
- $0.15
- Output / 1M
- $0.60
- Context
- 128K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
GPT-4o-mini
1.5x cheaper for a typical chat
Higher intelligence score
Qwen3 VL 8B Instruct
7.3 vs 6.7 on Artificial Analysis
Larger context window
Qwen3 VL 8B Instruct
262K vs 128K tokens
Newer release
Qwen3 VL 8B Instruct
Released October 14, 2025
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Qwen3 VL 8B Instruct7.3GPT-4o-mini6.7Coding index
Qwen3 VL 8B InstructNot availableGPT-4o-mini11.4Agentic index
Qwen3 VL 8B InstructNot availableGPT-4o-miniNot availablePricing
Qwen3 VL 8B Instruct vs GPT-4o-mini API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Qwen3 VL 8B Instruct | GPT-4o-mini |
|---|---|---|
| Input tokensPer 1M tokens | $0.25 | $0.15 |
| Output tokensPer 1M tokens | $0.75 | $0.60 |
| Cached input (read)Per 1M tokens | $0.12 | $0.08 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Qwen3 VL 8B Instruct$0.88GPT-4o-mini$0.60Coding task
30K in, 4K out
Qwen3 VL 8B Instruct$10.50$6.60 cachedGPT-4o-mini$6.90$4.65 cachedLong document summary
150K in, 2K out
Qwen3 VL 8B Instruct$39$19.50 cachedGPT-4o-mini$23.70$12.45 cachedSpecs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Qwen3 VL 8B Instruct | GPT-4o-mini |
|---|---|---|
| Context window | 262K tokens | 128K tokens |
| Max output | 236K tokens | 16K tokens |
| Input types | Image, Text | File, Image, Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | October 14, 2025 | July 18, 2024 |
Features
Supported features
API features available for each model.
| Metric | Qwen3 VL 8B Instruct | GPT-4o-mini |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Not supported | Not supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
Qwen3 VL 8B InstructView full model details- Qwen3 VL 8B Instruct vs Qwen3 30B A3B Instruct 2507
- Qwen3 VL 8B Instruct vs Llama 3.3 70B Instruct
- Qwen3 VL 8B Instruct vs Qwen3 Coder Next
- Qwen3 VL 8B Instruct vs Qwen3 Next 80B A3B Instruct
- Qwen3 VL 8B Instruct vs Qwen3 VL 30B A3B Thinking
- Qwen3 VL 8B Instruct vs Qwen3 VL 30B A3B Instruct
- Qwen3 VL 8B Instruct vs Solar Pro 3
- Qwen3 VL 8B Instruct vs Qwen2.5 72B Instruct
- Qwen3 VL 8B Instruct vs Sonar
- Qwen3 VL 8B Instruct vs Qwen3 30B A3B
- Qwen3 VL 8B Instruct vs Hermes 3 405B Instruct
- Qwen3 VL 8B Instruct vs Hermes 3 70B Instruct
- Qwen3 VL 8B Instruct vs Qwen2.5 Coder 32B Instruct
- Qwen3 VL 8B Instruct vs Saba
- Qwen3 VL 8B Instruct vs GPT-3.5 Turbo
GPT-4o-miniView full model details- GPT-4o-mini vs GPT-6 Luna Pro
- GPT-4o-mini vs GPT-5 Nano
- GPT-4o-mini vs GPT-4.1 Mini
- GPT-4o-mini vs Gemma 3 27B
- GPT-4o-mini vs Llama 3.3 70B Instruct
- GPT-4o-mini vs GPT-4.1 Nano
- GPT-4o-mini vs Llama 4 Maverick
- GPT-4o-mini vs Qwen3 VL 30B A3B Instruct
- GPT-4o-mini vs Sonar
- GPT-4o-mini vs Nova Lite 1.0
- GPT-4o-mini vs Qwen3 30B A3B
- GPT-4o-mini vs Hermes 3 70B Instruct
- GPT-4o-mini vs Llama 3.1 70B Instruct
- GPT-4o-mini vs Llama 3.2 3B Instruct
- GPT-4o-mini vs Hermes 3 405B Instruct
- GPT-4o-mini vs Solar Pro 3
- GPT-4o-mini vs GPT-3.5 Turbo
- GPT-4o-mini vs GLM 4.5V
- GPT-4o-mini vs Nova Pro 1.0
- GPT-4o-mini vs Qwen2.5 Coder 32B Instruct
- GPT-4o-mini vs Saba
- GPT-4o-mini vs Reka Flash 3
Our take
GPT-4o-mini is 1.5x cheaper for a typical chat. Qwen3 VL 8B Instruct scores higher on the Artificial Analysis Intelligence Index (7.3 vs 6.7). Qwen3 VL 8B Instruct has the larger context window (262K vs 128K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Qwen3 VL 8B Instruct or GPT-4o-mini cheaper?
GPT-4o-mini is cheaper for a typical chat (2K input tokens and 500 output tokens). GPT-4o-mini costs $0.15 per 1M input tokens and $0.60 per 1M output tokens, while Qwen3 VL 8B Instruct costs $0.25 per 1M input tokens and $0.75 per 1M output tokens.
Which has a bigger context window, Qwen3 VL 8B Instruct or GPT-4o-mini?
Qwen3 VL 8B Instruct supports up to 262K tokens of context, compared with 128K for GPT-4o-mini.
Is Qwen3 VL 8B Instruct smarter than GPT-4o-mini?
On the Artificial Analysis Intelligence Index, Qwen3 VL 8B Instruct scores 7.3 and GPT-4o-mini scores 6.7, putting Qwen3 VL 8B Instruct ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Qwen3 VL 8B Instruct and GPT-4o-mini at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Qwen3 VL 8B Instruct and GPT-4o-mini answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Qwen3 VL 8B Instruct and GPT-4o-mini at the same time. Compare the answers side by side and keep the best one.