Qwen3.5 35B A3B vs Phi-4 Multimodal Instruct

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokens$0.14Deepinfra · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$1.00Deepinfra · Sep 22, 2026Not reported
Context windowMaximum documented tokens262K131K
Model facts checkedAug 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

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldQwen3.5-35B-A3BPhi-4-multimodal-instruct
DeveloperQwenMicrosoft
FamilyQwen3 5 35b A3bPhi 4 Multimodal Instruct
ModelQwen3.5-35B-A3BPhi-4-multimodal-instruct
VersionQwen3.5-35B-A3BPhi-4-multimodal-instruct
Lifecycleactiveactive
ReleasedUnknown2025-02-26
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image, Audio
Output modalitiesTextText
Context window262K131K
Total parameters36B5.6B
Active parameters3BUnknown
Licenseapache-2.0mit
Open weightsYesYes
API availableYesUnknown
Self-hostableYesYes
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)Unknown
Capabilitieschat, generation, reasoning, toolschat, generation

Qwen3.5 35B A3B Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDQwen/Qwen3.5-35B-A3B

Phi-4 Multimodal Instruct Capabilities

chatgeneration
Serving providers0
Canonical IDmicrosoft/Phi-4-multimodal-instruct

Primary Evidence

Sources and Freshness

Questions

Qwen3.5 35B A3B vs Phi-4 Multimodal Instruct FAQs

Is Qwen3.5 35B A3B or Phi-4 Multimodal Instruct better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.5 35B A3B 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 35B A3B or Phi-4 Multimodal Instruct?+

Only Qwen3.5 35B A3B has a directly sourced input price: $0.14 per million tokens. Only Qwen3.5 35B A3B has a directly sourced output price: $1.00 per million tokens.

Which has a larger context window, Qwen3.5 35B A3B or Phi-4 Multimodal Instruct?+

Qwen3.5 35B A3B has the larger sourced context window. Qwen3.5 35B A3B supports 262K and Phi-4 Multimodal Instruct supports 131K.

Which performs better in benchmarks, Qwen3.5 35B A3B 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 35B A3B or Phi-4 Multimodal Instruct be self-hosted?+

Both models have the same recorded self-hosting status: supported. Qwen3.5 35B A3B is open weight; Phi-4 Multimodal Instruct is open weight.

Can Qwen3.5 35B A3B and Phi-4 Multimodal Instruct understand images?+

Qwen3.5 35B A3B 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 35B A3B or Phi-4 Multimodal Instruct?+

Neither has a larger sourced maximum output. Qwen3.5 35B A3B is — and Phi-4 Multimodal Instruct is —.

Do Qwen3.5 35B A3B and Phi-4 Multimodal Instruct support reasoning and tool use?+

Qwen3.5 35B A3B: 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 35B A3B or Phi-4 Multimodal Instruct?+

Qwen3.5 35B A3B has 4 sourced provider routes; Phi-4 Multimodal Instruct has 0, so Qwen3.5 35B A3B has broader tracked availability.

Which offers better value, Qwen3.5 35B A3B 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.

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