Claude Haiku 4.5 vs o3-mini
Claude Haiku 4.5 (Azure AI Foundry, 200,000-token context) versus o3-mini (Azure OpenAI, 200,000-token context). o3-mini is cheaper by 8% on a blended token mix. Claude Haiku 4.5 uniquely supports function calling and vision input. Across 5 public benchmarks we tracked, Claude Haiku 4.5 wins 3 and o3-mini wins 2. 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 Haiku 4.5 vs o3-mini
Claude Haiku 4.5 and o3-mini 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.
On capability surface area, the models diverge: Claude Haiku 4.5 supports function calling where the other does not; Claude Haiku 4.5 supports vision input where the other does not; Claude Haiku 4.5 supports pdf input 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.
Across 5 public benchmarks, Claude Haiku 4.5 leads on 3 and o3-mini leads on 2. The widest gap is on arena-elo, where Claude Haiku 4.5 scores 64.0 points higher. Benchmarks are noisy and task-dependent — a model that leads on arena-elo may trail on code generation. The safest approach is to run both models on your own golden set before treating any benchmark as decisive.
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-haiku-4-5
provider: azure-ai-foundry
fallback:
model: o3-mini
provider: azure-openai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Claude Haiku 4.5 | o3-mini | |
|---|---|---|
| Input price | $1.00/M | $1.10/M |
| Output price | $5.00/M | $4.40/M |
| Context window | 200,000 | 200,000 |
| Max output | 64,000 | 100,000 |
| 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 Haiku 4.5 | o3-mini | Delta |
|---|---|---|---|
| Startup 10K requests/day | $600 /mo | $594 /mo | $6.00/mo |
| Mid-market 100K requests/day | $6,000 /mo | $5,940 /mo | $60.00/mo |
| Enterprise 1M requests/day | $60,000 /mo | $59,400 /mo | $600/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 Haiku 4.5 accepts image input natively, the other doesn't.
Your agent calls tools or APIs — Claude Haiku 4.5 supports function calling natively, the other model needs a parser shim.
On arena-elo, Claude Haiku 4.5 scores 64.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
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 Haiku 4.5, switching to o3-mini means re-architecting that path (and vice versa).
- • Function calling
- • Vision input
- • PDF input
Capabilities both share (4)
- ✓ Streaming
- ✓ Structured output (JSON schema)
- ✓ Prompt caching
- ✓ Native reasoning mode
Benchmark winners — by the numbers
For each public benchmark that has scores for both models, the higher score and the size of the gap. Benchmarks are noisy — treat anything under a 2-point delta as effectively tied.
| Benchmark | Claude Haiku 4.5 | o3-mini | Winner | Δ |
|---|---|---|---|---|
| arena-elo | 1412.0 | 1348.0 | Claude Haiku 4.5 | +64.0 |
| gpqa-diamond | 55.2 | 77.0 | o3-mini | +21.8 |
| humaneval | 89.5 | 89.0 | Claude Haiku 4.5 | ~0 |
| mmlu-pro | 72.4 | 79.7 | o3-mini | +7.3 |
| swe-bench-verified | 52.0 | 49.3 | Claude Haiku 4.5 | +2.7 |
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 64,000 on Claude Haiku 4.5 vs 100,000 on o3-mini. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Claude Haiku 4.5 has capabilities o3-mini lacks: Function calling, Vision input, PDF input. Switching to o3-mini means re-architecting any flow that depends on these.
- Provider changes from Azure AI Foundry 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 Haiku 4.5 vs o3-mini 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 Haiku 4.5 primary, mirror 20% of traffic to o3-mini 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 Haiku 4.5 vs o3-mini
Which is cheaper, Claude Haiku 4.5 or o3-mini? ▾
o3-mini is cheaper by roughly 8% on a blended input + output token mix. Input prices are $1.00/M for Claude Haiku 4.5 versus $1.10/M for o3-mini; output prices are $5.00/M versus $4.40/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 Haiku 4.5 versus o3-mini? ▾
Claude Haiku 4.5 supports up to 200,000 tokens of context. o3-mini supports up to 200,000 tokens. o3-mini has the larger window by a factor of 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Claude Haiku 4.5 and o3-mini both support tool calling? ▾
Only Claude Haiku 4.5 supports native function calling. The other model can still be made to call tools through a structured-output workaround, but the reliability of that pattern is lower than native support.
Can Claude Haiku 4.5 and o3-mini process images? ▾
Claude Haiku 4.5 accepts native image input. o3-mini does not — you would need to route image-heavy workloads through Claude Haiku 4.5 or add a separate vision model in front of o3-mini.
Which model supports prompt caching for cost reduction? ▾
Both Claude Haiku 4.5 and o3-mini 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 Haiku 4.5 over o3-mini? ▾
Your inputs include screenshots, diagrams, or product photos — Claude Haiku 4.5 accepts image input natively, the other doesn't. Your agent calls tools or APIs — Claude Haiku 4.5 supports function calling natively, the other model needs a parser shim. On arena-elo, Claude Haiku 4.5 scores 64.0 points higher — if your workload pattern matches that benchmark's task shape, the gap is meaningful.
When should I choose o3-mini over Claude Haiku 4.5? ▾
On the data this page surfaces, o3-mini is the right pick when Claude Haiku 4.5'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 Haiku 4.5 against o3-mini 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.