Aya 23 8B vs Phi-4 Mini
Aya 23 8B (2024) and Phi-4 Mini (2024) are general-purpose language models from Cohere and Microsoft Research. Aya 23 8B ships a not-yet-sourced context window, while Phi-4 Mini ships a not-yet-sourced context window. On Google-Proof Q&A, Aya 23 8B leads by 20.0 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 Aya 23 8B when provider fit matters.
Decision scorecard
Local evidence first| Signal | Aya 23 8B | Phi-4 Mini |
|---|---|---|
| Decision fit | Coding and Classification | Classification |
| Context window | — | — |
| Cheapest output | - | $0.15/1M tokens |
| Provider routes | 0 tracked | 3 tracked |
| Shared benchmarks | Google-Proof Q&A leader | 2 rows |
Decision tradeoffs
- Aya 23 8B leads the largest shared benchmark signal on Google-Proof Q&A by 20.0 points.
- Local decision data tags Aya 23 8B for Coding and Classification.
- Phi-4 Mini has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Phi-4 Mini for Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Aya 23 8B
Unavailable
No complete token price in local provider data
Phi-4 Mini
$77.50
Cheapest tracked route: Novita AI
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Aya 23 8B and Phi-4 Mini; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Phi-4 Mini and Aya 23 8B; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-02-21 | 2024-12-13 |
| Context window | — | — |
| Parameters | 8B | 3.8B |
| Architecture | decoder only | - |
| License | Unknown | Microsoft Research |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Aya 23 8B | Phi-4 Mini |
|---|---|---|
| Input price | - | $0.05/1M tokens |
| Output price | - | $0.15/1M tokens |
| Providers | - |
Capabilities
| Capability | Aya 23 8B | Phi-4 Mini |
|---|---|---|
| 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 | Aya 23 8B | Phi-4 Mini |
|---|---|---|
| Google-Proof Q&A | 45.2 | 25.2 |
| Massive Multitask Language Understanding | 72.8 | 67.3 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Aya 23 8B at 45.2 and Phi-4 Mini at 25.2, with Aya 23 8B ahead by 20.0 points; Massive Multitask Language Understanding has Aya 23 8B at 72.8 and Phi-4 Mini at 67.3, with Aya 23 8B ahead by 5.5 points. The largest visible gap is 20.0 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: Aya 23 8B has no token price sourced yet and Phi-4 Mini has $0.05/1M input tokens. Provider availability is 0 tracked routes versus 3. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Aya 23 8B when provider fit are central to the workload. Choose Phi-4 Mini when provider fit and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Is Aya 23 8B or Phi-4 Mini open source?
Aya 23 8B is listed under Unknown. Phi-4 Mini is listed under Microsoft Research. 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 Aya 23 8B and Phi-4 Mini?
Aya 23 8B is available on the tracked providers still being sourced. Phi-4 Mini is available on Fireworks AI, NVIDIA NIM, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Aya 23 8B over Phi-4 Mini?
Phi-4 Mini is safer overall; choose Aya 23 8B when provider fit matters. If your workload also depends on provider fit, start with Aya 23 8B; if it depends on provider fit, run the same evaluation with Phi-4 Mini.
What is the main difference between Aya 23 8B and Phi-4 Mini?
Aya 23 8B and Phi-4 Mini 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.