qwen3-coder
vercel_ai_gateway · chat model
qwen3-coder is listed here as a chat model from vercel_ai_gateway. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.
Quick read
Best for
Use this page when you need a fast view of cost, context size, and supported features before testing the model in your own workload.
Things to verify
Always check the provider page for discounts, cache pricing, region rules, and any model limits that may not appear in public metadata.
Pricing
| Item | Price |
|---|---|
| Input | $0.4000 / 1M tokens |
| Output | $1.6000 / 1M tokens |
| Embedding | $0.4000 / 1M tokens |
Token limits
Capabilities
| Capability | Supported |
|---|---|
| Vision | — |
| Function calling | ✅ |
| Parallel function calling | — |
| Tool choice | ✅ |
| Prompt caching | — |
| Reasoning | — |
| Response schema | — |
| System messages | — |
| Audio input | — |
| Audio output | — |
| Web search | — |
| PDF input | — |
| Video input | — |
Benchmarks
Most benchmark rows are attached to the base model family rather than this provider route. Open benchmark explorer
| Benchmark | Score | Metric | Scope | Checked | Source |
|---|---|---|---|---|---|
| Terminal-Bench 2.0 | 23.9 * | score | Base model: Qwen3-Coder (Qwen3-Coder-480B-A35B-Instruct) | 2026-05-31 | Link |
| SWE-bench Pro | 38.7 | score | Base model: Qwen3-Coder (Qwen3-Coder-480B-A35B-Instruct) | 2026-05-31 | Link |
| Evasion Bench | 78.16 | score | Base model: Qwen3-Coder (Qwen3-Coder-480B-A35B-Instruct) | 2026-05-31 | Link |
| Artificial Analysis Intelligence Index | 24.8 | score | Base model: qwen3-coder (qwen/qwen3-coder) | 2026-05-31 | Link |
| Artificial Analysis Coding Index | 24.6 | score | Base model: qwen3-coder (qwen/qwen3-coder) | 2026-05-31 | Link |
| Artificial Analysis Agentic Index | 18.3 | score | Base model: qwen3-coder (qwen/qwen3-coder) | 2026-05-31 | Link |
Sources
| Source links | |
| Pricing data | LiteLLM model cost map |
| Synced at | 2026-05-28 |
Docs
| Official docs |
Similar models
This list is ranked by overall similarity. Use filters to emphasize the lens that matters most for the replacement you are making.
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| qwen3-coder vercel_ai_gateway | In $0.4000 / 1M tokens Out $1.6000 / 1M tokens | text
Output: text | Function callingTool choice | 66.5K | Current model Reference row |
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| GLM-4.5 deepinfra | In $0.4000 / 1M tokens Out $1.6000 / 1M tokens | text
Output: text | Function callingTool choice | 131.1K | Text covered Overall 93% |
| Qwen3-Coder-480B-A35B-Instruct deepinfra | In $0.4000 / 1M tokens Out $1.6000 / 1M tokens | text
Output: text | Function callingTool choice | 262.1K | Text covered Overall 89% |
| Kimi-K2-Instruct deepinfra | In $0.5000 / 1M tokens Out $2.0000 / 1M tokens | text
Output: text | Function callingTool choice | 131.1K | Text covered Overall 89% |
| glm-4.5-air vercel_ai_gateway | In $0.2000 / 1M tokens Out $1.1000 / 1M tokens | text
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| glm-4.5 vercel_ai_gateway | In $0.6000 / 1M tokens Out $2.2000 / 1M tokens | text
Output: text | Function callingTool choice | 131.1K | Same provider Overall 87% |
| DeepSeek-R1-0528 deepinfra | In $0.5000 / 1M tokens Out $2.1500 / 1M tokens | text
Output: text | Function callingTool choice | 163.8K | Text covered Overall 86% |
| gpt-3.5-turbo Azure | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Text covered Overall 83% |
| gpt-35-turbo Azure | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Text covered Overall 83% |
| gpt-3.5-turbo vercel_ai_gateway | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Same provider Overall 83% |
| kimi-k2 vercel_ai_gateway | In $0.5500 / 1M tokens Out $2.2000 / 1M tokens | text
Output: text | Function callingTool choice | 16.4K | Same provider Overall 83% |
| grok-3-mini-fast vercel_ai_gateway | In $0.6000 / 1M tokens Out $4.0000 / 1M tokens | text
Output: text | Function callingTool choice | 131.1K | Same provider Overall 83% |
| grok-3-mini vercel_ai_gateway | In $0.3000 / 1M tokens Out $0.5000 / 1M tokens | text
Output: text | Function callingTool choice | 131.1K | Same provider Overall 83% |
| Llama-3.3-70B-Instruct azure_ai | In $0.7100 / 1M tokens Out $0.7100 / 1M tokens | text
Output: text | Function callingTool choice | 2.0K | Text covered Overall 75% |
| Phi-4 azure_ai | In $0.1250 / 1M tokens Out $0.5000 / 1M tokens | text
Output: text | Function callingTool choice | 16.4K | Text covered Overall 75% |
| mistral-medium-2505 azure_ai | In $0.4000 / 1M tokens Out $2.0000 / 1M tokens | text image
Output: text | Function callingTool choice | 8.2K | Text covered Overall 73% |
| gpt-35-turbo-16k-0613 Azure | In $3.0000 / 1M tokens Out $4.0000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Text covered Overall 72% |
| gpt-4-0613 Azure | In $30.0000 / 1M tokens Out $60.0000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Text covered Overall 66% |
| gpt-4.1-mini Azure | In $0.4000 / 1M tokens Out $1.6000 / 1M tokens | text image
Output: text | Function callingTool choice | 32.8K | Text covered Overall 63% |
| gpt-4.1-mini-2025-04-14 Azure | In $0.4000 / 1M tokens Out $1.6000 / 1M tokens | text image
Output: text | Function callingTool choice | 32.8K | Text covered Overall 63% |
| GPT-4.1 mini OpenAI | In $0.4000 / 1M tokens Out $1.6000 / 1M tokens | text imagepdf
Output: text | Function callingTool choice | 32.8K | Text covered Overall 61% |
| qwen3-vl-235b-a22b-instruct dashscope | In $0.4000 / 1M tokens Out $1.6000 / 1M tokens | text
Output: unknown | Function callingTool choice | 32.8K | Partial I/O overlap Overall 44% Missing text |
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