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GPT-5.2 vs Qwerky 72b
Pricing is only the shared seller matrix below. Best rates and catalog map are separate sections, so no chart is embedded in pricing. Open the calculator for the same workload on each side.
Qwerky 72b is cheaper across the board — 100% cheaper on input and 100% cheaper on output.
Pricing matrix
Shared sellers only. Each row is a host that lists both models. Dollar amounts are per 1M tokens. Lowest in each pair of columns is emphasized.
| Provider | GPT-5.2 in | GPT-5.2 out | Qwerky 72b in | Qwerky 72b out |
|---|---|---|---|---|
| Azure | $1.75 | $14.00 | N/A | N/A |
| Featherless | N/A | N/A | N/A | N/A |
| Github Copilot | N/A | N/A | N/A | N/A |
| Gmi | $1.75 | $14.00 | N/A | N/A |
| OpenAI | $1.75 | $14.00 | N/A | N/A |
| Openrouter | $1.75 | $14.00 | N/A | N/A |
Best listed rates (this pair)
Same "best row in current pricing" logic as model pages: a compact view of the lowest input and output we show for each side. This is not the seller matrix.
Catalog map · OpenAI
Each chart uses real catalog rows that include both list input and list output ($/1M). Larger green dots are the models on this comparison page; accent dots are other active models from that provider.
OpenAI · input vs output
Scatter chart: X = input $/M, Y = output $/M. This page's models are labeled on the green dots; hover others for detail.
One dot per catalog row that has both list prices. Green dots are labeled with the models on this page; hover any dot for name and prices. Upper-right means higher cost on both axes ($/1M).
Specifications
Each column header is the full model name. Tint marks the favorable value where we compute a winner.
- Context window
- 272,000 tokens
- 32,768 tokens
- Max output tokens
- 128,000 tokens
- 4,096 tokens
- Vision
- Yes
- No
- Function calling
- Yes
- No
- Streaming
- No
- No
- Release date
- Dec 2025
- N/A
Cost calculator
Same requests, input tokens, and output tokens for both columns. Pick the provider row that matches how you buy.
When to pick which
Short takeaways: no cards, just the points. Validate with your own workloads.
Long-context tasks
Use GPT-5.2. Its 272K context window handles large codebases and documents better than 32K.
Long output (reports, code files)
Use GPT-5.2. Its 128K max output limit reduces the need to split requests.
Frequently asked questions
More comparisons
Other popular head-to-head matchups.