Mixtral 8x7B vs Phi 3.5 MoE Instruct
Mixtral 8x7B (2023) and Phi 3.5 MoE Instruct (2024) are compact production models from MistralAI and Microsoft Research. Mixtral 8x7B ships a 32k-token context window, while Phi 3.5 MoE Instruct ships a 128k-token context window. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.50/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Mixtral 8x7B is ~233% cheaper at $0.15/1M; pay for Phi 3.5 MoE Instruct only for long-context analysis.
Decision scorecard
Local evidence first| Signal | Mixtral 8x7B | Phi 3.5 MoE Instruct |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | Coding and Classification | Long context |
| Context window | 32k | 128k |
| Cheapest output | $0.45/1M tokens | $0.50/1M tokens |
| Provider routes | 18 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Mixtral 8x7B has the lower cheapest tracked output price at $0.45/1M tokens.
- Mixtral 8x7B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Mixtral 8x7B for Coding and Classification.
- Phi 3.5 MoE Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Phi 3.5 MoE Instruct for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Mixtral 8x7B
$233
Cheapest tracked route/tier: Mistral AI Studio
Phi 3.5 MoE Instruct
$525
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $293. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Phi 3.5 MoE Instruct is $0.05/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Mixtral 8x7B is $0.05/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-12-11 | 2024-08-20 |
| Context window | 32k | 128k |
| Parameters | 8x7B | 16x3.8B (42B, 6.6B active) |
| Architecture | mixture of experts | decoder only |
| License | Apache 2.0(OSI) | MIT(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | 2023-12 | 2023-10 |
Pricing and availability
| Pricing attribute | Mixtral 8x7B | Phi 3.5 MoE Instruct |
|---|---|---|
| Input price | $0.15/1M tokens | $0.50/1M tokens |
| Output price | $0.45/1M tokens | $0.50/1M tokens |
| Providers |
Capabilities
| Capability | Mixtral 8x7B | Phi 3.5 MoE Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Mixtral 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider, while Phi 3.5 MoE Instruct lists $0.50/1M input and $0.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $0.26 per million blended tokens. Availability is 18 providers versus 1, so concentration risk also matters.
Choose Mixtral 8x7B when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Phi 3.5 MoE Instruct when long-context analysis and larger context windows 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 or Phi 3.5 MoE Instruct?
Phi 3.5 MoE Instruct supports 128k tokens, while Mixtral 8x7B supports 32k 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 or Phi 3.5 MoE Instruct?
Mixtral 8x7B is cheaper on tracked token pricing. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Phi 3.5 MoE Instruct costs $0.50/1M input and $0.50/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x7B or Phi 3.5 MoE Instruct open source?
Mixtral 8x7B is listed under Apache 2.0. Phi 3.5 MoE Instruct is listed under MIT. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Mixtral 8x7B and Phi 3.5 MoE Instruct?
Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API (Deprecated). Phi 3.5 MoE Instruct is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Mixtral 8x7B over Phi 3.5 MoE Instruct?
Mixtral 8x7B is ~233% cheaper at $0.15/1M; pay for Phi 3.5 MoE Instruct only for long-context analysis. If your workload also depends on provider fit, start with Mixtral 8x7B; if it depends on long-context analysis, run the same evaluation with Phi 3.5 MoE Instruct.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.