Phi 4 Multimodal
nano-gpt/phi-4-multimodal-instructPor nano-gpt · lanzado 2025-07-26
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding3
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability1/10
Agents11
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit10/15
- Provider availability1/10
JSON / structured output20
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume20/20
Cost efficiency81
- Headline price (log-scaled)81/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Production-readiness65
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.41 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $0.81 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.20 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $0.67 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $0.91 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalle de precios
Precio recomendado de nano-gpt · phi-4-multimodal-instruct
Disponible en 1 proveedores
| Proveedor | ID de modelo del proveedor | Entrada / 1M | Salida / 1M | Contexto | Lanzado |
|---|---|---|---|---|---|
| NanoGPT nano-gpt | phi-4-multimodal-instruct | $0.070 | $0.110 | 128K | 2025-07-26 |
Frequently asked questions
How much does Phi 4 Multimodal cost?
Phi 4 Multimodal costs $0.070 per 1M input tokens and $0.110 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Phi 4 Multimodal?
Phi 4 Multimodal has a context window of 128K tokens, with a max output of 16K tokens per reply. This is the total combined size of prompt + completion.
Does Phi 4 Multimodal support tool calling?
No. Phi 4 Multimodal does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Phi 4 Multimodal support structured output / JSON mode?
No. Phi 4 Multimodal does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.
Can Phi 4 Multimodal accept image input?
No. Phi 4 Multimodal only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Phi 4 Multimodal open-weight?
No. Phi 4 Multimodal is a proprietary model — only nano-gpt (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Phi 4 Multimodal?
If Phi 4 Multimodal doesn't fit, consider Brave (Answers), Exa (Research), Auto model (Basic). Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More nano-gpt models
- Brave (Answers)$5.00 in / $5.00 out
- Exa (Research)$2.50 in / $2.50 out
- Auto model (Basic)$10.00 in / $19.99 out
- Jamba Mini$0.20 in / $0.41 out
- Yi Large$3.20 in / $3.20 out
Última actualización:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.