Decision snapshot
| Decision factor | Qwen3.8-Flash | GPT-5.6 Sol |
|---|---|---|
| Text and agent workflows | Evaluate | Evaluate |
| Benchmarks | Vendor-reported; live above | Verify independent evaluation |
| Deployment control | Verify weight/license status | Verify current terms |
| Non-text modalities | Verify provider capabilities | Verify provider capabilities |
| Input $ / 1M tokens | $0.16 (live above) | $1.25 |
| Output $ / 1M tokens | $0.47 (live above) | $10.00 |
| Headline benchmark | Vendor-reported; live above | GPT-5 MMLU: 92.0% (OpenAI, vendor-reported) |
| Pricing provenance | OpenAI API pricing — https://openai.com/api/pricing/ |
Rival pricing anchor (vendor-reported)
GPT-5 tier. "GPT-5.6 Sol" is not in the OpenAI public catalog; reference GPT-5.
Benchmark rules
- Compare only the same evaluation and harness version.
- Label vendor-reported results.
- Record token budget and tool policy.
- Do not infer production reliability from one benchmark.
Ready to test the workflow?
Create account & add creditsStrengths and tradeoffs
Select the model against a representative prompt set, latency target, output budget, tool-calling requirements, and data-control constraints.
Workload recommendation
| Workload | How to choose |
|---|---|
| Budget-sensitive coding | Compare task success per dollar. |
| Long-context analysis | Test retrieval and citation accuracy. |
| Multimodal input | Choose a model/provider that explicitly supports it. |
| Regulated data | Review retention, residency, and deployment terms. |
Frequently asked questions
Can I try Qwen3.8-Flash before integrating it?
Use the OneInfer Qwen3.8-Flash launcher to open a prepared prompt in the authenticated playground. Availability is checked against the current model catalog.
How should I treat benchmark claims?
Check the provenance label and harness version. Vendor-reported and independently verified results are deliberately shown as different evidence classes.
Put Qwen3.8-Flash to work
Fund a controlled evaluation, start with a prepared prompt, and measure quality and cost on your own workload.