Context Window

Compare

Gpt 4o Search Preview 2025 03 11 vs Qwen Coder

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 4o Search Preview 2025 03 11

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen Coder

Tool calling

Context window

1M

1,000,000 tokens · ~750K 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 4o Search Preview 2025 03 11128K
Qwen Coder1M

Qwen Coder has about 7.8× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen Coder has 681% more context capacity (1000K vs 128K tokens). Qwen Coder is 88% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen Coder. Its 1000K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen Coder. Input tokens are 88% 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 4o Search Preview 2025 03 11Qwen Coder
Context window128,000 tokens (128K)1,000,000 tokens (1000K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesNo
Batch APIYesNo
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 4o Search Preview 2025 03 11 inGpt 4o Search Preview 2025 03 11 outQwen Coder inQwen Coder out
Alibaba Cloud——$0.300/M$1.50/M
Openai$2.50/M$10.00/M——

Frequently asked questions

Qwen Coder has a larger context window: 1000K 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