Compare
Gpt 5 1 Chat Latest vs Qwen3 Vl 235b A22b Instruct 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.
Model
Qwen3 Vl 235b A22b Instruct Fp8
Context window
262K
262,144 tokens · ~197K 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.
Qwen3 Vl 235b A22b Instruct Fp8 has about 2× the context window of the other in this pair.
Qwen3 Vl 235b A22b Instruct Fp8 has 104% more context capacity (262K vs 128K tokens). Qwen3 Vl 235b A22b Instruct Fp8 is 76% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Qwen3 Vl 235b A22b Instruct Fp8. Its 262K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Qwen3 Vl 235b A22b Instruct Fp8. Input tokens are 76% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Gpt 5 1 Chat Latest | Qwen3 Vl 235b A22b Instruct Fp8 |
|---|---|---|
| Context window | 128,000 tokens (128K) | 262,144 tokens (262K) |
| Max output tokens | 16,384 tokens (16K) | 16,384 tokens (16K) |
| Speed tier | Balanced | Balanced |
| Vision | Yes | Yes |
| Function calling | No | No |
| 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 | Gpt 5 1 Chat Latest in | Gpt 5 1 Chat Latest out | Qwen3 Vl 235b A22b Instruct Fp8 in | Qwen3 Vl 235b A22b Instruct Fp8 out |
|---|---|---|---|---|
| Gmi | — | — | $0.300/M | $1.40/M |
| Openai | $1.25/M | $10.00/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