Phi 4 vs GPT-3.5 Turbo Instruct compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
GPT-3.5 Turbo Instruct — full profile ›
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
| Spec | Phi 4 | GPT-3.5 Turbo Instruct |
|---|---|---|
| Maker | Microsoft | OpenAI |
| Type | Open-weight | Proprietary |
| Context window | 16K tokens | 4K tokens |
| Input price | $0.07/M · free self-host | $1.5/M |
| Output price | $0.14/M · free self-host | $2/M |
| Vision / multimodal | No | No |
| Tool / function calling | No | No |
| Self-hostable | Yes | No (API only) |
| License | MIT | Proprietary |
| Capability | Phi 4 | GPT-3.5 Turbo Instruct |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✗ | ✗ |
| Tool / function calling | ✗ | ✗ |
| 1M+ context window | ✗ | ✗ |
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
| Criterion | Phi 4 | GPT-3.5 Turbo Instruct |
|---|---|---|
| Cost-efficiency | 5.0 | 4.5 |
| Context window | 2.0 | 2.0 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 2.0 | 2.0 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses are needed. At 14 billion...
This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.
Phi 4 is open-weight and competitive on many tasks, but GPT-3.5 Turbo Instruct may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Yes. Phi 4 has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. GPT-3.5 Turbo Instruct is API-only and cannot be self-hosted.
Phi 4 costs $0.14/M output vs $2/M for GPT-3.5 Turbo Instruct — roughly 14x cheaper via API, and free if you self-host.
Choose Phi 4 if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose GPT-3.5 Turbo Instruct if you want frontier capability through a managed API with zero infrastructure to run.
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