DeepSeek R1 vs Phi 3.5 MoE Instruct
DeepSeek R1 (2025) and Phi 3.5 MoE Instruct (2024) are frontier reasoning models from DeepSeek and Microsoft Research. DeepSeek R1 ships a 128k-token context window, while Phi 3.5 MoE Instruct ships a 128k-token context window. On pricing, DeepSeek R1 costs $0.10/1M 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.
DeepSeek R1 is ~400% cheaper at $0.10/1M; pay for Phi 3.5 MoE Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 | Phi 3.5 MoE Instruct |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | general production evaluation |
| Decision fit | Coding, RAG, and Agents | Long context |
| Context window | 128k | 128k |
| Cheapest output | $0.30/1M tokens | $0.50/1M tokens |
| Provider routes | 14 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- DeepSeek R1 has the lower cheapest tracked output price at $0.30/1M tokens.
- DeepSeek R1 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek R1 uniquely exposes Reasoning, Structured outputs, and Code execution in local model data.
- Local decision data tags DeepSeek R1 for Coding, RAG, and Agents.
- 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.
DeepSeek R1
$155
Cheapest tracked route/tier: Bitdeer AI
Phi 3.5 MoE Instruct
$525
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $370. 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 $0.20/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Reasoning, Structured outputs, and Code execution before moving production traffic.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- DeepSeek R1 is $0.20/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- DeepSeek R1 adds Reasoning, Structured outputs, and Code execution in local capability data.
Specs
Pricing and availability
| Pricing attribute | DeepSeek R1 | Phi 3.5 MoE Instruct |
|---|---|---|
| Input price | $0.10/1M tokens | $0.50/1M tokens |
| Output price | $0.30/1M tokens | $0.50/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 | Phi 3.5 MoE Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | Yes | 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 differs most on reasoning mode: DeepSeek R1, structured outputs: DeepSeek R1, and code execution: DeepSeek R1. 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, DeepSeek R1 lists $0.10/1M input and $0.30/1M 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 DeepSeek R1 lower by about $0.34 per million blended tokens. Availability is 14 providers versus 1, so concentration risk also matters.
Choose DeepSeek R1 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Phi 3.5 MoE Instruct when provider fit 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, DeepSeek R1 or Phi 3.5 MoE Instruct?
DeepSeek R1 supports 128k tokens, while Phi 3.5 MoE Instruct supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, DeepSeek R1 or Phi 3.5 MoE Instruct?
DeepSeek R1 is cheaper on tracked token pricing. DeepSeek R1 costs $0.10/1M input and $0.30/1M 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 DeepSeek R1 or Phi 3.5 MoE Instruct open source?
DeepSeek R1 is listed under MIT. 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.
Which is better for reasoning mode, DeepSeek R1 or Phi 3.5 MoE Instruct?
DeepSeek R1 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, DeepSeek R1 or Phi 3.5 MoE Instruct?
DeepSeek R1 has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek R1 and Phi 3.5 MoE Instruct?
DeepSeek R1 is available on DeepSeek Platform, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Phi 3.5 MoE Instruct is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.