Compare
Grok 4 1 Fast Non Reasoning 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
Grok 4 1 Fast Non Reasoning
Context window
131K
131,072 tokens · ~98K 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.
Grok 4 1 Fast Non Reasoning has about 1× the context window of the other in this pair.
Grok 4 1 Fast Non Reasoning has 2% more context capacity (131K vs 128K tokens). Grok 4 1 Fast Non Reasoning is 79% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Grok 4 1 Fast Non Reasoning. Its 131K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Grok 4 1 Fast Non Reasoning. Input tokens are 79% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Grok 4 1 Fast Non Reasoning. Its 131K 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 | Grok 4 1 Fast Non Reasoning | Meta Llama3 1 70b Instruct |
|---|---|---|
| Context window | 131,072 tokens (131K) | 128,000 tokens (128K) |
| Max output tokens | 131,072 tokens (131K) | 2,048 tokens (2K) |
| Speed tier | Deep | Deep |
| Vision | No | 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 | Grok 4 1 Fast Non Reasoning in | Grok 4 1 Fast Non Reasoning out | Meta Llama3 1 70b Instruct in | Meta Llama3 1 70b Instruct out |
|---|---|---|---|---|
| Aws Bedrock | — | — | $0.990/M | $0.990/M |
| Azure | $0.200/M | $0.500/M | — | — |
| Google Vertex | $0.200/M | $0.500/M | — | — |
| Xai | $0.200/M | $0.500/M | — | — |
Frequently asked questions
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Use a smaller model.
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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