Context Window

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Deepseek Prover V2 671b vs Glm 5 2 Fp8

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.

Deepseek

Model

Deepseek Prover V2 671b

Context window

160K

160,000 tokens · ~120K words

Model page
Z Ai

Model

Glm 5 2 Fp8

Context window

1.0M

1,048,576 tokens · ~786K words

Model page

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.

Deepseek Prover V2 671b160K
Glm 5 2 Fp81.0M

Glm 5 2 Fp8 has about 6.6× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5 2 Fp8 has 555% more context capacity (1048K vs 160K tokens). Deepseek Prover V2 671b is 64% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5 2 Fp8. Its 1048K context fits entire documents without chunking (vs 160K).

  • RAG / high-volume retrieval

    Use Deepseek Prover V2 671b. Input tokens are 64% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecDeepseek Prover V2 671bGlm 5 2 Fp8
Context window160,000 tokens (160K)1,048,576 tokens (1048K)
Max output tokens160,000 tokens (160K)N/A
Speed tierFastBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoNo
Release dateN/AN/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.

ProviderDeepseek Prover V2 671b inDeepseek Prover V2 671b outGlm 5 2 Fp8 inGlm 5 2 Fp8 out
Gmi$0.500/M$2.18/M$1.40/M$4.40/M
Novita$0.700/M$2.50/M——

Frequently asked questions

Glm 5 2 Fp8 has a larger context window: 1048K tokens vs 160K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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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.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model