Mixtral 8x7B Instruct v0.1 vs Qwen3.5-9B
Mixtral 8x7B Instruct v0.1 (2023) and Qwen3.5-9B (2026) are compact production models from MistralAI and Alibaba. Mixtral 8x7B Instruct v0.1 ships a 33K-token context window, while Qwen3.5-9B ships a 262K-token context window. On pricing, Qwen3.5-9B costs $0.1/1M input tokens versus $0.15/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3.5-9B is ~50% cheaper at $0.1/1M; pay for Mixtral 8x7B Instruct v0.1 only for provider fit.
Decision scorecard
Local evidence first| Signal | Mixtral 8x7B Instruct v0.1 | Qwen3.5-9B |
|---|---|---|
| Decision fit | General | RAG, Agents, and Long context |
| Context window | 33K | 262K |
| Cheapest output | $0.45/1M tokens | $0.15/1M tokens |
| Provider routes | 5 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Mixtral 8x7B Instruct v0.1 has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3.5-9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-9B has the lower cheapest tracked output price at $0.15/1M tokens.
- Qwen3.5-9B uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Qwen3.5-9B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Mixtral 8x7B Instruct v0.1
$233
Cheapest tracked route: DeepInfra
Qwen3.5-9B
$118
Cheapest tracked route: Together AI
Estimated monthly gap: $115. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Together AI; start route-level A/B tests there.
- Qwen3.5-9B is $0.3/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Qwen3.5-9B adds Vision, Multimodal, and Function calling in local capability data.
- Provider overlap exists on Together AI; start route-level A/B tests there.
- Mixtral 8x7B Instruct v0.1 is $0.3/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-12-10 | 2026-03-02 |
| Context window | 33K | 262K |
| Parameters | 56B | 9B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Pricing attribute | Mixtral 8x7B Instruct v0.1 | Qwen3.5-9B |
|---|---|---|
| Input price | $0.15/1M tokens | $0.1/1M tokens |
| Output price | $0.45/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Mixtral 8x7B Instruct v0.1 | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Qwen3.5-9B, multimodal input: Qwen3.5-9B, function calling: Qwen3.5-9B, tool use: Qwen3.5-9B, and structured outputs: Qwen3.5-9B. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, Mixtral 8x7B Instruct v0.1 lists $0.15/1M input and $0.45/1M output tokens, while Qwen3.5-9B lists $0.1/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-9B lower by about $0.13 per million blended tokens. Availability is 5 providers versus 3, so concentration risk also matters.
Choose Mixtral 8x7B Instruct v0.1 when provider fit and broader provider choice are central to the workload. Choose Qwen3.5-9B when long-context analysis, larger context windows, and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Mixtral 8x7B Instruct v0.1 or Qwen3.5-9B?
Qwen3.5-9B supports 262K tokens, while Mixtral 8x7B Instruct v0.1 supports 33K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Mixtral 8x7B Instruct v0.1 or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Mixtral 8x7B Instruct v0.1 costs $0.15/1M input and $0.45/1M output tokens. Qwen3.5-9B costs $0.1/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x7B Instruct v0.1 or Qwen3.5-9B open source?
Mixtral 8x7B Instruct v0.1 is listed under Open Source. Qwen3.5-9B is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, Mixtral 8x7B Instruct v0.1 or Qwen3.5-9B?
Qwen3.5-9B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Mixtral 8x7B Instruct v0.1 or Qwen3.5-9B?
Qwen3.5-9B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Mixtral 8x7B Instruct v0.1 and Qwen3.5-9B?
Mixtral 8x7B Instruct v0.1 is available on Together AI, OctoML (Deprecated), AWS Bedrock, IBM watsonx, and DeepInfra. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.