Context Window

Compare

Gpt 5 1 Chat Latest vs Llama 3 1 8b

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.

Openai

Model

Gpt 5 1 Chat Latest

Image input

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Llama 3 1 8b

Context window

131K

131,000 tokens · ~98K 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.

Gpt 5 1 Chat Latest128K
Llama 3 1 8b131K

Llama 3 1 8b has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 3 1 8b has 2% more context capacity (131K vs 128K tokens). Llama 3 1 8b is 97% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 3 1 8b. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Llama 3 1 8b. Input tokens are 97% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGpt 5 1 Chat LatestLlama 3 1 8b
Context window128,000 tokens (128K)131,000 tokens (131K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierBalancedFast
VisionYesNo
Function callingNoNo
Extended thinkingYesNo
Prompt cachingYesNo
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.

ProviderGpt 5 1 Chat Latest inGpt 5 1 Chat Latest outLlama 3 1 8b inLlama 3 1 8b out
Novita——$0.020/M$0.050/M
Nscale——$0.030/M$0.030/M
Openai$1.25/M$10.00/M——
Ovhcloud——$0.100/M$0.100/M
Perplexity——$0.200/M$0.200/M

Frequently asked questions

Llama 3 1 8b has a larger context window: 131K tokens vs 128K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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.

Calculate your savingsLearn about Mem0

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