Context Window

Compare

Meta Llama3 1 70b Instruct vs Qwen3 235b A22b Instruct 2507 Tput

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.

Meta

Model

Meta Llama3 1 70b Instruct

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen3 235b A22b Instruct 2507 Tput

Tool calling

Context window

262K

262,000 tokens · ~197K 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.

Meta Llama3 1 70b Instruct128K
Qwen3 235b A22b Instruct 2507 Tput262K

Qwen3 235b A22b Instruct 2507 Tput has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 235b A22b Instruct 2507 Tput has 104% more context capacity (262K vs 128K tokens). Qwen3 235b A22b Instruct 2507 Tput is 79% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 235b A22b Instruct 2507 Tput. Its 262K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen3 235b A22b Instruct 2507 Tput. Input tokens are 79% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMeta Llama3 1 70b InstructQwen3 235b A22b Instruct 2507 Tput
Context window128,000 tokens (128K)262,000 tokens (262K)
Max output tokens2,048 tokens (2K)N/A
Speed tierDeepBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
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.

ProviderMeta Llama3 1 70b Instruct inMeta Llama3 1 70b Instruct outQwen3 235b A22b Instruct 2507 Tput inQwen3 235b A22b Instruct 2507 Tput out
Aws Bedrock$0.990/M$0.990/M——
Together Ai——$0.200/M$6.00/M

Frequently asked questions

Qwen3 235b A22b Instruct 2507 Tput has a larger context window: 262K 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