Phi 4 Multimodal Instruct vs Qwen2-VL-72B-Instruct
Phi 4 Multimodal Instruct (2025) and Qwen2-VL-72B-Instruct (2025) are compact production models from Microsoft Research and Alibaba. Phi 4 Multimodal Instruct ships a 128k-token context window, while Qwen2-VL-72B-Instruct ships a 32k-token context window. On MMMU Pro, Qwen2-VL-72B-Instruct leads by 20.8 pts. On pricing, both list $0.90/1M input and $0.90/1M output tokens on the cheapest tracked route. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Pick Qwen2-VL-72B-Instruct for reasoning; token pricing is tied, so keep Phi 4 Multimodal Instruct only for already-validated prompts or route constraints.
Decision scorecard
Local evidence first| Signal | Phi 4 Multimodal Instruct | Qwen2-VL-72B-Instruct |
|---|---|---|
| Best for | multimodal apps and provider-routed production | multimodal apps |
| Decision fit | Long context and Vision | Vision |
| Context window | 128k | 32k |
| Cheapest output | $0.90/1M tokens | $0.90/1M tokens |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 1 shared | MMMU Pro leader |
Decision tradeoffs
- Phi 4 Multimodal Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Phi 4 Multimodal Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Phi 4 Multimodal Instruct for Long context and Vision.
- Qwen2-VL-72B-Instruct holds a shared-benchmark lead on MMMU Pro, ahead by 20.8 points.
- Local decision data tags Qwen2-VL-72B-Instruct for Vision.
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
Qwen2-VL-72B-Instruct
$945
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $0.00. 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.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2025-01-01 |
| Context window | 128k | 32k |
| Parameters | 5.6B | 72B |
| Architecture | Decoder Only | Decoder Only |
| License | MITOSI-approved | Apache 2.0OSI-approved |
| Openness | Open source | Open source |
| Commercial use | Commercial use: permitted | Commercial use: permitted |
| Knowledge cutoff | 2024-06 | 2023-06 |
Pricing and availability
| Pricing attribute | Phi 4 Multimodal Instruct | Qwen2-VL-72B-Instruct |
|---|---|---|
| Input price | $0.90/1M tokens | $0.90/1M tokens |
| Output price | $0.90/1M tokens | $0.90/1M tokens |
| Providers |
Capabilities
| Capability | Phi 4 Multimodal Instruct | Qwen2-VL-72B-Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| 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
| Benchmark | Phi 4 Multimodal Instruct | Qwen2-VL-72B-Instruct |
|---|---|---|
| MMMU Pro | 38.5 | 59.3 |
Deep dive
On shared benchmark coverage, MMMU Pro has Phi 4 Multimodal Instruct at 38.5 and Qwen2-VL-72B-Instruct at 59.3, with Qwen2-VL-72B-Instruct ahead by 20.8 points. The largest visible gap is 20.8 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 is close: both models cover vision and multimodal input. 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, Phi 4 Multimodal Instruct lists $0.90/1M input and $0.90/1M output tokens on the cheapest tracked provider, while Qwen2-VL-72B-Instruct lists $0.90/1M input and $0.90/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend is tied on the cheapest tracked routes. Availability is 3 providers versus 1, so concentration risk also matters.
Choose Phi 4 Multimodal Instruct when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Qwen2-VL-72B-Instruct when vision-heavy evaluation 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 Qwen2-VL-72B-Instruct?
Phi 4 Multimodal Instruct supports 128k tokens, while Qwen2-VL-72B-Instruct 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, Phi 4 Multimodal Instruct or Qwen2-VL-72B-Instruct?
Neither is cheaper on tracked token pricing. Both list $0.90/1M input and $0.90/1M output tokens on the cheapest tracked route. Provider discounts, batch pricing, or route-specific tiers can still change the final bill.
Is Phi 4 Multimodal Instruct or Qwen2-VL-72B-Instruct open source?
Phi 4 Multimodal Instruct is listed under MIT. Qwen2-VL-72B-Instruct 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 Qwen2-VL-72B-Instruct?
Both Phi 4 Multimodal Instruct and Qwen2-VL-72B-Instruct expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Phi 4 Multimodal Instruct or Qwen2-VL-72B-Instruct?
Both Phi 4 Multimodal Instruct and Qwen2-VL-72B-Instruct expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Phi 4 Multimodal Instruct and Qwen2-VL-72B-Instruct?
Phi 4 Multimodal Instruct is available on Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Qwen2-VL-72B-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.