Computer Use preview vs GPT 5.2
Computer Use preview (Azure OpenAI, 8,192-token context) versus GPT 5.2 (Azure OpenAI, 272,000-token context). Computer Use preview is cheaper by 5% on a blended token mix. GPT 5.2 uniquely supports pdf input and prompt caching. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Computer Use preview vs GPT 5.2
Computer Use preview and GPT 5.2 are priced within 5% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
GPT 5.2 ships a 272,000-token context window, 33.2x larger than Computer Use preview's 8,192 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 8,192 tokens, the extra context on GPT 5.2 is insurance you may never use — and Computer Use preview may win on other axes.
On capability surface area, the models diverge: GPT 5.2 supports pdf input where the other does not; GPT 5.2 supports prompt caching where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: gpt-5-2
provider: azure-openai
fallback:
model: computer-use-preview
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Computer Use preview | GPT 5.2 | |
|---|---|---|
| Input price | $3.00/M | $1.75/M |
| Output price | $12.00/M | $14.00/M |
| Context window | 8,192 | 272,000 |
| Max output | 1,024 | 128,000 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | — | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Jun 2, 2026 | Jun 2, 2026 |
Benchmark comparison
Side-by-side public benchmark scores. Greener bar = winner.
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Computer Use preview | GPT 5.2 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $1,620 /mo | $1,365 /mo | $255/mo |
| Mid-market 100K requests/day | $16,200 /mo | $13,650 /mo | $2,550/mo |
| Enterprise 1M requests/day | $162,000 /mo | $136,500 /mo | $25,500/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your workload needs long context — GPT 5.2 fits 272,000 tokens versus the other model's 8,192, enough headroom for full books, large codebases, or 100+ page documents in one shot.
You re-send the same large system prompt across requests — GPT 5.2 supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Computer Use preview, switching to GPT 5.2 means re-architecting that path (and vice versa).
- • PDF input
- • Prompt caching
Capabilities both share (6)
- ✓ Function calling
- ✓ Parallel tool calls
- ✓ Vision input
- ✓ Streaming
- ✓ Structured output (JSON schema)
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 3220% when moving from Computer Use preview (8,192) to GPT 5.2 (272,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 1,024 on Computer Use preview vs 128,000 on GPT 5.2. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- GPT 5.2 has capabilities Computer Use preview lacks: PDF input, Prompt caching. Worth wiring through the agent design before commit.
How to A/B test Computer Use preview vs GPT 5.2 in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Computer Use preview primary, mirror 20% of traffic to GPT 5.2 in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Computer Use preview vs GPT 5.2
Which is cheaper, Computer Use preview or GPT 5.2? ▾
Computer Use preview is cheaper by roughly 5% on a blended input + output token mix. Input prices are $3.00/M for Computer Use preview versus $1.75/M for GPT 5.2; output prices are $12.00/M versus $14.00/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Computer Use preview versus GPT 5.2? ▾
Computer Use preview supports up to 8,192 tokens of context. GPT 5.2 supports up to 272,000 tokens. GPT 5.2 has the larger window by a factor of 33.2x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Computer Use preview and GPT 5.2 both support tool calling? ▾
Yes — both Computer Use preview and GPT 5.2 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Which model supports prompt caching for cost reduction? ▾
GPT 5.2 supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, GPT 5.2 gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Computer Use preview over GPT 5.2? ▾
On the data this page surfaces, Computer Use preview is the right pick when GPT 5.2's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
When should I choose GPT 5.2 over Computer Use preview? ▾
Your workload needs long context — GPT 5.2 fits 272,000 tokens versus the other model's 8,192, enough headroom for full books, large codebases, or 100+ page documents in one shot. You re-send the same large system prompt across requests — GPT 5.2 supports prompt caching, cutting input cost on repeat hits.
How do I A/B test Computer Use preview against GPT 5.2 in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.