Claude Opus 4 (2025-05-14) vs o1-preview (2024-09-12)
Claude Opus 4 (2025-05-14) (Google Vertex AI, 200,000-token context) versus o1-preview (2024-09-12) (Azure OpenAI, 128,000-token context). o1-preview (2024-09-12) is cheaper by 8% on a blended token mix. Claude Opus 4 (2025-05-14) uniquely supports vision input and pdf input. o1-preview (2024-09-12) uniquely supports parallel tool calls. 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 — Claude Opus 4 (2025-05-14) vs o1-preview (2024-09-12)
Claude Opus 4 (2025-05-14) and o1-preview (2024-09-12) are priced within 8% 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.
Claude Opus 4 (2025-05-14) ships a 200,000-token context window, 1.6x larger than o1-preview (2024-09-12)'s 128,000 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 128,000 tokens, the extra context on Claude Opus 4 (2025-05-14) is insurance you may never use — and o1-preview (2024-09-12) may win on other axes.
On capability surface area, the models diverge: Claude Opus 4 (2025-05-14) supports vision input where the other does not; Claude Opus 4 (2025-05-14) supports pdf input where the other does not; Claude Opus 4 (2025-05-14) supports structured output (json schema) 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: claude-opus-4-20250514
provider: vertex-ai
fallback:
model: eu-o1-preview-2024-09-12
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Claude Opus 4 (2025-05-14) | o1-preview (2024-09-12) | |
|---|---|---|
| Input price | $15.00/M | $16.50/M |
| Output price | $75.00/M | $66.00/M |
| Context window | 200,000 | 128,000 |
| Max output | 32,000 | 32,768 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | ✓ | — |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | — |
| Pricing verified | Aug 6, 2026 | Aug 6, 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 | Claude Opus 4 (2025-05-14) | o1-preview (2024-09-12) | Delta |
|---|---|---|---|
| Startup 10K requests/day | $9,000 /mo | $8,910 /mo | $90.00/mo |
| Mid-market 100K requests/day | $90,000 /mo | $89,100 /mo | $900/mo |
| Enterprise 1M requests/day | $900,000 /mo | $891,000 /mo | $9,000/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 inputs include screenshots, diagrams, or product photos — Claude Opus 4 (2025-05-14) accepts image input natively, the other doesn't.
Your tasks involve multi-step planning or math-heavy reasoning — Claude Opus 4 (2025-05-14) ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
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 Claude Opus 4 (2025-05-14), switching to o1-preview (2024-09-12) means re-architecting that path (and vice versa).
- • Vision input
- • PDF input
- • Structured output (JSON schema)
- • Native reasoning mode
- • Parallel tool calls
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Prompt caching
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 36% when moving from Claude Opus 4 (2025-05-14) (200,000) to o1-preview (2024-09-12) (128,000). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 32,000 on Claude Opus 4 (2025-05-14) vs 32,768 on o1-preview (2024-09-12). Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Claude Opus 4 (2025-05-14) has capabilities o1-preview (2024-09-12) lacks: Vision input, PDF input, Structured output (JSON schema), Native reasoning mode. Switching to o1-preview (2024-09-12) means re-architecting any flow that depends on these.
- o1-preview (2024-09-12) has capabilities Claude Opus 4 (2025-05-14) lacks: Parallel tool calls. Worth wiring through the agent design before commit.
- Provider changes from Google Vertex AI to Azure OpenAI. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test Claude Opus 4 (2025-05-14) vs o1-preview (2024-09-12) 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 Claude Opus 4 (2025-05-14) primary, mirror 20% of traffic to o1-preview (2024-09-12) 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 — Claude Opus 4 (2025-05-14) vs o1-preview (2024-09-12)
Which is cheaper, Claude Opus 4 (2025-05-14) or o1-preview (2024-09-12)? ▾
o1-preview (2024-09-12) is cheaper by roughly 8% on a blended input + output token mix. Input prices are $15.00/M for Claude Opus 4 (2025-05-14) versus $16.50/M for o1-preview (2024-09-12); output prices are $75.00/M versus $66.00/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 Claude Opus 4 (2025-05-14) versus o1-preview (2024-09-12)? ▾
Claude Opus 4 (2025-05-14) supports up to 200,000 tokens of context. o1-preview (2024-09-12) supports up to 128,000 tokens. Claude Opus 4 (2025-05-14) has the larger window by a factor of 1.6x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Claude Opus 4 (2025-05-14) and o1-preview (2024-09-12) both support tool calling? ▾
Yes — both Claude Opus 4 (2025-05-14) and o1-preview (2024-09-12) 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.
Can Claude Opus 4 (2025-05-14) and o1-preview (2024-09-12) process images? ▾
Claude Opus 4 (2025-05-14) accepts native image input. o1-preview (2024-09-12) does not — you would need to route image-heavy workloads through Claude Opus 4 (2025-05-14) or add a separate vision model in front of o1-preview (2024-09-12).
Which model supports prompt caching for cost reduction? ▾
Both Claude Opus 4 (2025-05-14) and o1-preview (2024-09-12) support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.
When should I choose Claude Opus 4 (2025-05-14) over o1-preview (2024-09-12)? ▾
Your inputs include screenshots, diagrams, or product photos — Claude Opus 4 (2025-05-14) accepts image input natively, the other doesn't. Your tasks involve multi-step planning or math-heavy reasoning — Claude Opus 4 (2025-05-14) ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose o1-preview (2024-09-12) over Claude Opus 4 (2025-05-14)? ▾
On the data this page surfaces, o1-preview (2024-09-12) is the right pick when Claude Opus 4 (2025-05-14)'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.
How do I A/B test Claude Opus 4 (2025-05-14) against o1-preview (2024-09-12) 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.