Qwen3.5 397B A17B vs Phi-4 Multimodal Instruct
At a Glance
| Compare | Phi-4 Multimodal InstructMicrosoft | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.45Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $3.00Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 131K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Qwen3.5-397B-A17B | Phi-4-multimodal-instruct |
|---|---|---|
| Developer | Qwen | Microsoft |
| Family | Qwen3 5 397b A17b | Phi 4 Multimodal Instruct |
| Model | Qwen3.5-397B-A17B | Phi-4-multimodal-instruct |
| Version | Qwen3.5-397B-A17B | Phi-4-multimodal-instruct |
| Lifecycle | active | active |
| Released | 2026-02-15 | 2025-02-26 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Audio |
| Output modalities | Text | Text |
| Context window | 262K | 131K |
| Total parameters | 403.4B | 5.6B |
| Active parameters | 17B | Unknown |
| License | apache-2.0 | mit |
| Open weights | Yes | Yes |
| API available | Yes | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Unknown |
| Capabilities | chat, generation, reasoning, tools | chat, generation |
Qwen3.5 397B A17B Capabilities
Phi-4 Multimodal Instruct Capabilities
Primary Evidence
Sources and Freshness
Questions
Qwen3.5 397B A17B vs Phi-4 Multimodal Instruct FAQs
Is Qwen3.5 397B A17B or Phi-4 Multimodal Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 397B A17B and Phi-4 Multimodal Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.5 397B A17B or Phi-4 Multimodal Instruct?+
Only Qwen3.5 397B A17B has a directly sourced input price: $0.45 per million tokens. Only Qwen3.5 397B A17B has a directly sourced output price: $3.00 per million tokens.
Which has a larger context window, Qwen3.5 397B A17B or Phi-4 Multimodal Instruct?+
Qwen3.5 397B A17B has the larger sourced context window. Qwen3.5 397B A17B supports 262K and Phi-4 Multimodal Instruct supports 131K.
Which performs better in benchmarks, Qwen3.5 397B A17B or Phi-4 Multimodal Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.5 397B A17B or Phi-4 Multimodal Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. Qwen3.5 397B A17B is open weight; Phi-4 Multimodal Instruct is open weight.
Can Qwen3.5 397B A17B and Phi-4 Multimodal Instruct understand images?+
Qwen3.5 397B A17B is documented with image input; Phi-4 Multimodal Instruct is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Qwen3.5 397B A17B or Phi-4 Multimodal Instruct?+
Neither has a larger sourced maximum output. Qwen3.5 397B A17B is — and Phi-4 Multimodal Instruct is —.
Do Qwen3.5 397B A17B and Phi-4 Multimodal Instruct support reasoning and tool use?+
Qwen3.5 397B A17B: reasoning, tool calling, and image input. Phi-4 Multimodal Instruct: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Qwen3.5 397B A17B or Phi-4 Multimodal Instruct?+
Qwen3.5 397B A17B has 4 sourced provider routes; Phi-4 Multimodal Instruct has 0, so Qwen3.5 397B A17B has broader tracked availability.
Which offers better value, Qwen3.5 397B A17B or Phi-4 Multimodal Instruct?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.