Compare
Meta Llama3 1 70b Instruct vs Phi 4 Mini
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Model
Meta Llama3 1 70b Instruct
Context window
128K
128,000 tokens · ~96K words
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Phi 4 Mini has about 1× the context window of the other in this pair.
Phi 4 Mini has 2% more context capacity (131K vs 128K tokens). Phi 4 Mini is 92% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Phi 4 Mini. Its 131K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Phi 4 Mini. Input tokens are 92% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Phi 4 Mini. Its 4K max output lets you generate complete artifacts in one request.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Meta Llama3 1 70b Instruct | Phi 4 Mini |
|---|---|---|
| Context window | 128,000 tokens (128K) | 131,072 tokens (131K) |
| Max output tokens | 2,048 tokens (2K) | 4,096 tokens (4K) |
| Speed tier | Deep | Fast |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | N/A |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | Meta Llama3 1 70b Instruct in | Meta Llama3 1 70b Instruct out | Phi 4 Mini in | Phi 4 Mini out |
|---|---|---|---|---|
| Aws Bedrock | $0.990/M | $0.990/M | — | — |
| Azure | — | — | $0.075/M | $0.300/M |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model