Phi-4 Mini vs Starling LM 7B Beta
Phi-4 Mini (2024) and Starling LM 7B Beta (2024) are general-purpose language models from Microsoft Research and Nexusflow. Phi-4 Mini ships a not-yet-sourced context window, while Starling LM 7B Beta ships a not-yet-sourced context window. On Google-Proof Q&A, Starling LM 7B Beta leads by 24.5 pts. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Phi-4 Mini is safer overall; choose Starling LM 7B Beta when provider fit matters.
Decision scorecard
Local evidence first| Signal | Phi-4 Mini | Starling LM 7B Beta |
|---|---|---|
| Decision fit | Classification | Coding and Classification |
| Context window | — | — |
| Cheapest output | $0.15/1M tokens | - |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 2 rows | Google-Proof Q&A leader |
Decision tradeoffs
- Phi-4 Mini has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Phi-4 Mini for Classification.
- Starling LM 7B Beta leads the largest shared benchmark signal on Google-Proof Q&A by 24.5 points.
- Local decision data tags Starling LM 7B Beta for Coding and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Phi-4 Mini
$77.50
Cheapest tracked route: Novita AI
Starling LM 7B Beta
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Phi-4 Mini and Starling LM 7B Beta; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Starling LM 7B Beta and Phi-4 Mini; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-13 | 2024-02-05 |
| Context window | — | — |
| Parameters | 3.8B | 7B |
| Architecture | - | decoder only |
| License | Microsoft Research | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Phi-4 Mini | Starling LM 7B Beta |
|---|---|---|
| Input price | $0.05/1M tokens | - |
| Output price | $0.15/1M tokens | - |
| Providers |
Capabilities
| Capability | Phi-4 Mini | Starling LM 7B Beta |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
Benchmarks
| Benchmark | Phi-4 Mini | Starling LM 7B Beta |
|---|---|---|
| Google-Proof Q&A | 25.2 | 49.7 |
| Massive Multitask Language Understanding | 67.3 | 77.8 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Phi-4 Mini at 25.2 and Starling LM 7B Beta at 49.7, with Starling LM 7B Beta ahead by 24.5 points; Massive Multitask Language Understanding has Phi-4 Mini at 67.3 and Starling LM 7B Beta at 77.8, with Starling LM 7B Beta ahead by 10.5 points. The largest visible gap is 24.5 points on Google-Proof Q&A, 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 the core production surface. 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.
Pricing coverage is uneven: Phi-4 Mini has $0.05/1M input tokens and Starling LM 7B Beta has no token price sourced yet. Provider availability is 3 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Phi-4 Mini when provider fit and broader provider choice are central to the workload. Choose Starling LM 7B Beta when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Is Phi-4 Mini or Starling LM 7B Beta open source?
Phi-4 Mini is listed under Microsoft Research. Starling LM 7B Beta is listed under Unknown. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Phi-4 Mini and Starling LM 7B Beta?
Phi-4 Mini is available on Fireworks AI, NVIDIA NIM, and Novita AI. Starling LM 7B Beta is available on Cloudflare Workers AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Phi-4 Mini over Starling LM 7B Beta?
Phi-4 Mini is safer overall; choose Starling LM 7B Beta when provider fit matters. If your workload also depends on provider fit, start with Phi-4 Mini; if it depends on provider fit, run the same evaluation with Starling LM 7B Beta.
What is the main difference between Phi-4 Mini and Starling LM 7B Beta?
Phi-4 Mini and Starling LM 7B Beta differ most on context, provider coverage, capabilities, or pricing depending on the data currently sourced. Use the specs table first, then validate the model behavior with your own prompts.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.