
Qwen: Qwen3.8 27B (nitro)
qwen/qwen3.8-27b:nitro
Model weightsQwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick: Balanced (price + speed), Nitro (fastest), or Floor (cheapest).
| Provider | Input /M | Output /M | Cache read /M | Cache create 5m /M | Cache read 5m /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|---|---|
| $0.990 | $1.490 | $0.9900 | -- | -- | 0.29s | 945 tok/s | ||
| $0.450 | $3.000 | $0.1000 | -- | -- | 0.97s | 236 tok/s | ||
| $0.060 | $2.200 | $0.0590 | -- | -- | 0.43s | 142 tok/s | ||
| $0.040 | $2.300 | $0.0200 | -- | -- | 0.86s | 120 tok/s | ||
| $0.400 | $3.000 | $0.1500 | -- | -- | 0.50s | 118 tok/s | ||
| $0.150 | $1.600 | $0.0500 | -- | -- | 0.69s | 101 tok/s | ||
| $0.240 | $2.200 | $0.0500 | -- | -- | 0.72s | 94 tok/s | ||
| $0.040 | $2.500 | $0.0200 | -- | -- | 0.99s | 89 tok/s | ||
| $0.040 | $1.350 | $0.0180 | -- | -- | 1.26s | 78 tok/s | ||
| $0.125 | $2.000 | $0.0500 | -- | -- | 1.27s | 61 tok/s | ||
| $0.150 | $2.500 | $0.0500 | -- | -- | 1.63s | 60 tok/s | ||
| $0.450 | $3.200 | -- | -- | -- | 0.91s | 58 tok/s | ||
| $0.072 | $0.511 | $0.0080 | -- | -- | 0.93s | 54 tok/s | ||
| $0.225 | $1.980 | $0.0500 | -- | -- | 0.73s | 52 tok/s | ||
| $0.20$0.150 | $2.50$1.875 | $0.05$0.0375 | -- | -- | 0.87s | 51 tok/s | ||
| $0.420 | $3.000 | $0.0850 | -- | -- | 6.83s | 44 tok/s | ||
| $0.425 | $2.550 | $0.0850 | $0.53125 | $0.0425 | 0.79s | 42 tok/s | ||
| $0.450 | $3.200 | $0.0500 | -- | -- | 0.35s | 37 tok/s | ||
| $0.035 | $1.500 | $0.0175 | -- | -- | 2.87s | 35 tok/s | ||
| $0.039 | $1.250 | $0.0039 | -- | -- | 5.32s | 28 tok/s | ||
| $0.450 | $3.200 | $0.2500 | -- | -- | 5.01s | 27 tok/s | ||
| $0.040 | $1.950 | $0.0200 | -- | -- | 3.44s | 16 tok/s | ||
65% off | $0.50$0.175 | $3.00$1.050 | $0.10$0.0350 | -- | -- | 24.85s | 8 tok/s |
- Input $0.99 / Output $1.49945 tok/s100.00% uptime
- Input $0.45 / Output $3.00236 tok/s99.76% uptime
- Input $0.06 / Output $2.20142 tok/s99.68% uptime
- Input $0.04 / Output $2.30120 tok/s100.00% uptime
- Input $0.40 / Output $3.00118 tok/s99.97% uptime
- Input $0.15 / Output $1.60101 tok/s99.58% uptime
- Input $0.24 / Output $2.2094 tok/s100.00% uptime
- Input $0.04 / Output $2.5089 tok/s97.24% uptime
Input $0.04 / Output $1.3578 tok/s99.89% uptime- Input $0.125 / Output $2.0061 tok/s99.67% uptime
- Input $0.15 / Output $2.5060 tok/s100.00% uptime
- Input $0.45 / Output $3.2058 tok/s99.85% uptime
- Input $0.072 / Output $0.51154 tok/s99.79% uptime
- Input $0.225 / Output $1.9852 tok/s99.80% uptime
Input $0.15 / Output $1.87525% off51 tok/s100.00% uptime- Input $0.42 / Output $3.0044 tok/s100.00% uptime
- Input $0.425 / Output $2.5542 tok/s99.67% uptime
- Input $0.45 / Output $3.2037 tok/s98.49% uptime
Input $0.035 / Output $1.5035 tok/s99.24% uptime- Input $0.039 / Output $1.2528 tok/s99.26% uptime
- Input $0.45 / Output $3.2027 tok/s87.45% uptime
- Input $0.04 / Output $1.9516 tok/s97.07% uptime
- Input $0.175 / Output $1.0565% off8 tok/s95.25% uptime
Pricing
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Weighted average
Qwen3.8 27B — Price history
| $0.0130 | $1.249 | 74.1% | 4.5% | ||
| $0.0222 | $2.299 | 89.0% | 23.5% | ||
| $0.0229 | $1.349 | 77.5% | 10.1% | ||
| $0.0243 | $2.500 | 78.3% | 5.5% | ||
| $0.0273 | $1.949 | 63.3% | 0.5% | ||
| $0.0327 | $1.500 | 12.2% | 0.9% | ||
| $0.0593 | $2.200 | 67.4% | 15.2% | ||
| $0.0618 | $1.874 | 78.4% | 6.0% | ||
| $0.0755 | $1.600 | 74.4% | 2.1% | ||
| $0.0788 | $0.853 | 24.7% | 1.1% | ||
| $0.0800 | $2.199 | 84.2% | 4.1% | ||
| $0.0917 | $1.999 | 44.3% | 0.4% | ||
| $0.0924 | $1.049 | 59.0% | 2.6% | ||
| $0.1294 | $3.000 | 91.6% | 15.6% | ||
| $0.1336 | $2.500 | 16.3% | 1.5% | ||
| $0.1645 | $2.550 | 76.7% | 3.6% | ||
| $0.1695 | $1.978 | 31.6% | 0.6% | ||
| $0.1919 | $3.000 | 68.1% | 0.5% | ||
| $0.2747 | $3.000 | 50.1% | 0.9% | ||
| $0.2809 | $3.200 | 84.5% | 0.0% | ||
| $0.4498 | $3.200 | 0.3% | 0.0% | ||
| $0.4499 | $3.199 | 0.0% | 0.0% | ||
| $0.9899 | $1.489 | 26.4% | 0.6% |
- Effective in $0.0130 / Effective out $1.249Token share4.5%
- Effective in $0.0222 / Effective out $2.299Token share23.5%
- Effective in $0.0229 / Effective out $1.349Token share10.1%
- Effective in $0.0243 / Effective out $2.500Token share5.5%
- Effective in $0.0273 / Effective out $1.949Token share0.5%
- Effective in $0.0327 / Effective out $1.500Token share0.9%
- Effective in $0.0593 / Effective out $2.200Token share15.2%
- Effective in $0.0618 / Effective out $1.874Token share6.0%
- Effective in $0.0755 / Effective out $1.600Token share2.1%
- Effective in $0.0788 / Effective out $0.853Token share1.1%
- Effective in $0.0800 / Effective out $2.199Token share4.1%
- Effective in $0.0917 / Effective out $1.999Token share0.4%
- Effective in $0.0924 / Effective out $1.049Token share2.6%
- Effective in $0.1294 / Effective out $3.000Token share15.6%
- Effective in $0.1336 / Effective out $2.500Token share1.5%
- Effective in $0.1645 / Effective out $2.550Token share3.6%
- Effective in $0.1695 / Effective out $1.978Token share0.6%
- Effective in $0.1919 / Effective out $3.000Token share0.5%
- Effective in $0.2747 / Effective out $3.000Token share0.9%
- Effective in $0.2809 / Effective out $3.200Token share0.0%
- Effective in $0.4498 / Effective out $3.200Token share0.0%
- Effective in $0.4499 / Effective out $3.199Token share0.0%
- Effective in $0.9899 / Effective out $1.489Token share0.6%
Performance
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
P50, best across providers
P50, best provider
Uptime
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Availability over the last 3 days
Last 72 hoursAvailability over the last 24 hours
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.
Benchmarks
Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) Intelligence Index | 10.0 |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) Coding Index | 44.6 |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) Agentic Index | 22.4 |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) GPQA Diamond | 81.8% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) HLE | 12.1% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) AA-LCR | 69.3% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) τ-Bench Banking | 20.0% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) GDPval-AA | 29.1% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) CritPt | 0.3% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) SciCode | 36.2% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) Terminal-Bench 4.0 | 0.0% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) Terminal-Bench 2.1 | 49.1% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) AA-Omniscience Accuracy | 9.1% |
| Artificial Analysis | Qwen3.8 27B (Non-reasoning) AA-Omniscience Non-Hallucination Rate | 82.0% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) Intelligence Index | 20.1 |
| Artificial Analysis | Qwen3.8 27B (Xhigh) Coding Index | 68.1 |
| Artificial Analysis | Qwen3.8 27B (Xhigh) Agentic Index | 45.8 |
| Artificial Analysis | Qwen3.8 27B (Xhigh) GPQA Diamond | 90.5% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) HLE | 33.9% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) AA-LCR | 82.0% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) τ-Bench Banking | 48.0% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) GDPval-AA | 46.3% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) CritPt | 5.4% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) SciCode | 46.6% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) Terminal-Bench 4.0 | 5.6% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) Terminal-Bench 2.1 | 79.8% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) AA-Omniscience Accuracy | 17.3% |
| Artificial Analysis | Qwen3.8 27B (Xhigh) AA-Omniscience Non-Hallucination Rate | 71.4% |
| Artificial Analysis | Qwen3.8 27B (Low) Intelligence Index | 15.7 |
| Artificial Analysis | Qwen3.8 27B (Low) Coding Index | 58.2 |
| Artificial Analysis | Qwen3.8 27B (Low) Agentic Index | 38.9 |
| Artificial Analysis | Qwen3.8 27B (Low) GPQA Diamond | 84.5% |
| Artificial Analysis | Qwen3.8 27B (Low) HLE | 14.0% |
| Artificial Analysis | Qwen3.8 27B (Low) AA-LCR | 77.3% |
| Artificial Analysis | Qwen3.8 27B (Low) τ-Bench Banking | 32.2% |
| Artificial Analysis | Qwen3.8 27B (Low) GDPval-AA | 43.3% |
| Artificial Analysis | Qwen3.8 27B (Low) CritPt | 0.0% |
| Artificial Analysis | Qwen3.8 27B (Low) SciCode | 40.0% |
| Artificial Analysis | Qwen3.8 27B (Low) Terminal-Bench 4.0 | 2.5% |
| Artificial Analysis | Qwen3.8 27B (Low) Terminal-Bench 2.1 | 67.4% |
| Artificial Analysis | Qwen3.8 27B (Low) AA-Omniscience Accuracy | 18.3% |
| Artificial Analysis | Qwen3.8 27B (Low) AA-Omniscience Non-Hallucination Rate | 51.3% |
| Artificial Analysis | Qwen3.8 27B (Medium) Intelligence Index | 17.5 |
| Artificial Analysis | Qwen3.8 27B (Medium) Coding Index | 56.1 |
| Artificial Analysis | Qwen3.8 27B (Medium) Agentic Index | 44.4 |
| Artificial Analysis | Qwen3.8 27B (Medium) GPQA Diamond | 84.5% |
| Artificial Analysis | Qwen3.8 27B (Medium) HLE | 14.1% |
| Artificial Analysis | Qwen3.8 27B (Medium) AA-LCR | 79.7% |
| Artificial Analysis | Qwen3.8 27B (Medium) τ-Bench Banking | 47.4% |
| Artificial Analysis | Qwen3.8 27B (Medium) GDPval-AA | 44.5% |
| Artificial Analysis | Qwen3.8 27B (Medium) CritPt | 0.0% |
| Artificial Analysis | Qwen3.8 27B (Medium) SciCode | 39.0% |
| Artificial Analysis | Qwen3.8 27B (Medium) Terminal-Bench 4.0 | 5.1% |
| Artificial Analysis | Qwen3.8 27B (Medium) Terminal-Bench 2.1 | 65.2% |
| Artificial Analysis | Qwen3.8 27B (Medium) AA-Omniscience Accuracy | 17.9% |
| Artificial Analysis | Qwen3.8 27B (Medium) AA-Omniscience Non-Hallucination Rate | 34.6% |
| Vals AI | Qwen 3.8 27B Code Migration | 14.2% |
| Vals AI | Qwen 3.8 27B CyberBench v1.1 | 67.8% |
| Vals AI | Qwen 3.8 27B EMB | 59.7% |
| Vals AI | Qwen 3.8 27B Finance Agent (v2) | 48.6% |
| Vals AI | Qwen 3.8 27B GPQA Diamond | 88.9% |
| Vals AI | Qwen 3.8 27B Harvey's Legal Agent Benchmark v1 | 11.3% |
| Vals AI | Qwen 3.8 27B IOI | 39.1% |
| Vals AI | Qwen 3.8 27B LiveCodeBench | 84.0% |
| Vals AI | Qwen 3.8 27B LegalBench | 82.4% |
| Vals AI | Qwen 3.8 27B Legal Research Bench | 36.1% |
| Vals AI | Qwen 3.8 27B MedCode | 28.7% |
| Vals AI | Qwen 3.8 27B MedScribe | 83.8% |
| Vals AI | Qwen 3.8 27B MMLU Pro | 84.3% |
| Vals AI | Qwen 3.8 27B MMMU Pro | 83.9% |
| Vals AI | Qwen 3.8 27B MortgageTax | 64.9% |
| Vals AI | Qwen 3.8 27B ProgramBench | 0.0% |
| Vals AI | Qwen 3.8 27B ProofBench v1.1 | 16.0% |
| Vals AI | Qwen 3.8 27B SAGE v1 | 52.4% |
| Vals AI | Qwen 3.8 27B SkillsBench | 38.1% |
| Vals AI | Qwen 3.8 27B SWE-bench | 86.0% |
| Vals AI | Qwen 3.8 27B Tax Agent Bench | 30.9% |
| Vals AI | Qwen 3.8 27B TaxEval v2 | 70.8% |
| Vals AI | Qwen 3.8 27B Terminal-Bench 2.1 | 58.4% |
| Vals AI | Qwen 3.8 27B Terminal-Bench-Science | 0.0% |
| Vals AI | Qwen 3.8 27B Vals Index | 48.5% |
| Vals AI | Qwen 3.8 27B Vibe Code Bench v1.1 | 64.8% |
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Activity
Token volume and request traffic to this model over time.
API
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
Not currently available through EU in-region routing. In-region routing docs
Documentation
Guides, cookbooks, blog posts, and tools for building with Qwen: Qwen3.8 27B (nitro) on OpenRouter.
Frequently asked questions
Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled.
The Qwen3.8 27B documentation on OpenRouter includes the Self-Hosted LLM vs API: When Does Self-Hosting Pay Off blog post and the LLM Quantization: What fp8 and fp4 Do to Quality and Price blog post. The quickstart covers API keys and your first request, and the API reference lists every parameter.
Qwen3.8 27B costs $0.035/M input tokens and $1.50/M output tokens, with separate rates for Cache Read at $0.0175/M tokens.
Qwen3.8 27B has a 1,024,000 token context window. It supports up to 131,072 completion tokens.
Yes. Qwen3.8 27B accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
Qwen3.8 27B accepts text, images, and video as input and returns text.
Qwen3.8 27B is served by 23 providers on OpenRouter: Darkbloom, Chutes, NEAR AI, Ionstream, Modal, DekaLLM, Prime Intellect, Wafer and 15 more. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.
Qwen3.8 27B was released on August 14, 2026.