Phi 4 Multimodal Instruct vs Qwen3.5-35B-A3B
Phi 4 Multimodal Instruct (2025) and Qwen3.5-35B-A3B (2026) are frontier reasoning models from Microsoft Research and Alibaba. Phi 4 Multimodal Instruct ships a 128k-token context window, while Qwen3.5-35B-A3B ships a 262k-token context window. On MMMU Pro, Qwen3.5-35B-A3B leads by 36.6 pts. On pricing, Qwen3.5-35B-A3B costs $0.14/1M input tokens versus $0.90/1M 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.
Qwen3.5-35B-A3B is ~547% cheaper at $0.14/1M; pay for Phi 4 Multimodal Instruct only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Phi 4 Multimodal Instruct | Qwen3.5-35B-A3B |
|---|---|---|
| Best for | multimodal apps and provider-routed production | reasoning-heavy apps, tool-calling agents, and provider-routed production |
| Decision fit | Long context and Vision | Coding, RAG, and Agents |
| Context window | 128k | 262k |
| Cheapest output | $0.90/1M tokens | $1/1M tokens |
| Provider routes | 3 tracked | 2 tracked |
| Shared benchmarks | 1 rows | MMMU Pro leader |
Decision tradeoffs
- Phi 4 Multimodal Instruct has the lower cheapest tracked output price at $0.90/1M tokens.
- Phi 4 Multimodal Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Phi 4 Multimodal Instruct uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Phi 4 Multimodal Instruct for Long context and Vision.
- Qwen3.5-35B-A3B holds a shared-benchmark lead on MMMU Pro, ahead by 36.6 points.
- Qwen3.5-35B-A3B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-35B-A3B uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags Qwen3.5-35B-A3B for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Phi 4 Multimodal Instruct
$945
Cheapest tracked route/tier: Fireworks AI
Qwen3.5-35B-A3B
$361
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $584. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for Phi 4 Multimodal Instruct and Qwen3.5-35B-A3B; plan for SDK, billing, or endpoint changes.
- Qwen3.5-35B-A3B is $0.10/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- Qwen3.5-35B-A3B adds Reasoning, Function calling, and Tool use in local capability data.
- No overlapping tracked provider route is sourced for Qwen3.5-35B-A3B and Phi 4 Multimodal Instruct; plan for SDK, billing, or endpoint changes.
- Phi 4 Multimodal Instruct is $0.10/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Phi 4 Multimodal Instruct adds Vision and Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2026-02-24 |
| Context window | 128k | 262k |
| Parameters | 5.6B | 35B |
| Architecture | decoder only | mixture of experts |
| License | MIT(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | 2024-06 | - |
Pricing and availability
| Pricing attribute | Phi 4 Multimodal Instruct | Qwen3.5-35B-A3B |
|---|---|---|
| Input price | $0.90/1M tokens | $0.14/1M tokens |
| Output price | $0.90/1M tokens | $1/1M tokens |
| Providers |
Capabilities
| Capability | Phi 4 Multimodal Instruct | Qwen3.5-35B-A3B |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Phi 4 Multimodal Instruct | Qwen3.5-35B-A3B |
|---|---|---|
| MMMU Pro | 38.5 | 75.1 |
Deep dive
On shared benchmark coverage, MMMU Pro has Phi 4 Multimodal Instruct at 38.5 and Qwen3.5-35B-A3B at 75.1, with Qwen3.5-35B-A3B ahead by 36.6 points. The largest visible gap is 36.6 points on MMMU Pro, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Phi 4 Multimodal Instruct, multimodal input: Phi 4 Multimodal Instruct, reasoning mode: Qwen3.5-35B-A3B, function calling: Qwen3.5-35B-A3B, tool use: Qwen3.5-35B-A3B, and structured outputs: Qwen3.5-35B-A3B. 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, Phi 4 Multimodal Instruct lists $0.90/1M input and $0.90/1M output tokens on the cheapest tracked provider, while Qwen3.5-35B-A3B lists $0.14/1M input and $1/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-35B-A3B lower by about $0.50 per million blended tokens. Availability is 3 providers versus 2, so concentration risk also matters.
Choose Phi 4 Multimodal Instruct when vision-heavy evaluation and broader provider choice are central to the workload. Choose Qwen3.5-35B-A3B when reasoning depth, 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.
FAQ
Which has a larger context window, Phi 4 Multimodal Instruct or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B supports 262k tokens, while Phi 4 Multimodal 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, Phi 4 Multimodal Instruct or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B is cheaper on tracked token pricing. Phi 4 Multimodal Instruct costs $0.90/1M input and $0.90/1M output tokens. Qwen3.5-35B-A3B costs $0.14/1M input and $1/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Phi 4 Multimodal Instruct or Qwen3.5-35B-A3B open source?
Phi 4 Multimodal Instruct is listed under MIT. Qwen3.5-35B-A3B 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, Phi 4 Multimodal Instruct or Qwen3.5-35B-A3B?
Phi 4 Multimodal Instruct 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.
Which is better for multimodal input, Phi 4 Multimodal Instruct or Qwen3.5-35B-A3B?
Phi 4 Multimodal Instruct 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 Phi 4 Multimodal Instruct and Qwen3.5-35B-A3B?
Phi 4 Multimodal Instruct is available on Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Qwen3.5-35B-A3B is available on OpenRouter and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.