Compare
Glm 5 Code vs Meta Llama3 1 405b 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
Meta Llama3 1 405b 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.
Glm 5 Code has about 1.6× the context window of the other in this pair.
Glm 5 Code has 56% more context capacity (200K vs 128K tokens). Glm 5 Code is 77% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Glm 5 Code. Its 200K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Glm 5 Code. Input tokens are 77% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Glm 5 Code. Its 128K 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 | Glm 5 Code | Meta Llama3 1 405b Instruct |
|---|---|---|
| Context window | 200,000 tokens (200K) | 128,000 tokens (128K) |
| Max output tokens | 128,000 tokens (128K) | 4,096 tokens (4K) |
| Speed tier | Balanced | Deep |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | Yes | 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 | Glm 5 Code in | Glm 5 Code out | Meta Llama3 1 405b Instruct in | Meta Llama3 1 405b Instruct out |
|---|---|---|---|---|
| Aws Bedrock | — | — | $5.32/M | $16.00/M |
| Z Ai | $1.20/M | $5.00/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