Compare
Gpt 4 1 Mini 2025 04 14 vs Meta Llama3 1 70b Instruct
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
Gpt 4 1 Mini 2025 04 14
Context window
1.0M
1,047,576 tokens · ~786K words
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.
Gpt 4 1 Mini 2025 04 14 has about 8.2× the context window of the other in this pair.
Gpt 4 1 Mini 2025 04 14 has 718% more context capacity (1047K vs 128K tokens). Gpt 4 1 Mini 2025 04 14 is 59% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Gpt 4 1 Mini 2025 04 14. Its 1047K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Gpt 4 1 Mini 2025 04 14. Input tokens are 59% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Gpt 4 1 Mini 2025 04 14. Its 32K 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 | Gpt 4 1 Mini 2025 04 14 | Meta Llama3 1 70b Instruct |
|---|---|---|
| Context window | 1,047,576 tokens (1047K) | 128,000 tokens (128K) |
| Max output tokens | 32,768 tokens (32K) | 2,048 tokens (2K) |
| Speed tier | Fast | Deep |
| Vision | Yes | No |
| Function calling | Yes | Yes |
| Extended thinking | No | No |
| Prompt caching | Yes | 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 | Gpt 4 1 Mini 2025 04 14 in | Gpt 4 1 Mini 2025 04 14 out | Meta Llama3 1 70b Instruct in | Meta Llama3 1 70b Instruct out |
|---|---|---|---|---|
| Aws Bedrock | — | — | $0.990/M | $0.990/M |
| Azure | $0.400/M | $1.60/M | — | — |
| Openai | $0.400/M | $1.60/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