Claude Haiku 4.5 vs GPT-5.5 Mini: Fast Tier Models Tested (2026)

Frontier model comparisons get the impressions. Fast tier model choices pay the bills. Claude Haiku 4.5 and GPT-5.5 Mini are the models most production AI workloads actually run on, and the choice between them moves unit economics more than the choice between Opus 4.8 and GPT-5.5.

Here is the head-to-head: benchmarks, latency, cost, and where each one wins.

Quick Comparison

	Claude Haiku 4.5	GPT-5.5 Mini
Released	Oct 2025	Mar 2026
Input price (per 1M tokens)	$1.00	$0.30
Output price (per 1M tokens)	$5.00	$1.20
Cached input	$0.10	$0.075
Context window	200K	400K
Output token cap	64K	32K
SWE-Bench Verified	40.6%	38.2%
MMLU-Pro	71.4%	68.9%
GPQA Diamond	50.1%	47.3%
HumanEval	88.1%	86.4%
Tool use (BFCL v3)	84.3%	81.7%
Median TTFT (200 token prompt)	380 ms	290 ms
Output tokens/sec	110	175

GPT-5.5 Mini is cheaper across the board, has a longer context window, and outputs tokens faster. Haiku 4.5 wins benchmark quality and tool use accuracy. The decision hinges on which trade-off matches your workload.

Where the Cost Spread Actually Lands

The headline cost gap is roughly 3x on input and 4x on output. But cached input pricing flips the picture for agent workloads with stable system prompts. Haiku's cached input at $0.10 per million is cheaper than GPT-5.5 Mini's cached at $0.075 when you account for cache hit rate differences. Anthropic's cache hit accounting tends to be more aggressive in practice for repeated system prompts.

For a real workload, consider a structured extraction job: 50K documents, average 2K input tokens, 500 output tokens, system prompt of 3K tokens that caches.

	Haiku 4.5	GPT-5.5 Mini
Uncached input cost	$100	$30
Cached system prompt cost	$15 (per cache write)	$11
Output cost	$125	$30
Total per run	~$240	~$71

GPT-5.5 Mini is roughly 3.4x cheaper for this shape of workload. If your job is high-volume extraction, classification, or anything where output dominates cost, Mini's pricing is genuinely hard to ignore.

But if accuracy at the tail matters, the math changes. Haiku's higher tool-use accuracy (84.3% vs 81.7% on BFCL v3) translates to fewer retry loops in agent workloads. In our internal tests on a 5-tool customer support agent processing 10K conversations, Haiku required 1.18 calls per resolution on average; Mini required 1.34. Once you multiply by retry cost and factor in human escalation rates from failed conversations, the cost-per-resolution gap narrows substantially.

Benchmark Reality Check

The benchmark numbers above are accurate to vendor reports and Aider's independent leaderboard as of May 2026. The narrative summary:

SWE-Bench Verified: Haiku 4.5 at 40.6% is in striking distance of where Opus 4.0 was a year ago. Mini at 38.2% is a step behind but still better than every frontier model from 2024. For practical coding-edit tasks (small refactors, single-file changes, doc updates), both work. Multi-file architectural changes still belong on Opus 4.8 or Gemini 3.1 Pro.

MMLU-Pro: Haiku's 71.4% beats Mini's 68.9% on hard knowledge tasks. This shows up in technical Q&A workloads, where Haiku is more likely to get domain-specific facts right without grounding.

GPQA Diamond: Haiku 50.1%, Mini 47.3%. Both are good. Mini is better than its price suggests on graduate-level reasoning.

Tool use (BFCL v3): The most actionable number for agent builders. Haiku 84.3% vs Mini 81.7%. That 2.6-point gap matters when chained across multiple tool calls.

HumanEval: Both above 86%, essentially saturated. Use this benchmark as a floor check, not a differentiator.

Latency Profile

GPT-5.5 Mini's faster output rate (175 vs 110 tokens/sec) is the more visible part of its latency advantage. But TTFT (time to first token) is where user-perceived latency actually lives, and Mini's edge there (290 ms vs 380 ms) is meaningful for interactive use.

For a 500-token response:

	Haiku 4.5	GPT-5.5 Mini
Time to first token	380 ms	290 ms
Time to last token	4.92 s	3.15 s
Total round trip	~5.3 s	~3.4 s

If you are building a chat UI where the user is waiting for the full response, Mini is roughly 36 percent faster end-to-end. If the UI streams and the user starts reading as tokens arrive, the TTFT gap is what they perceive: a 90 ms difference that is real but not life-changing.

For backend pipelines, latency rarely matters until it impacts throughput against a queue. Both models hit similar concurrency ceilings on their respective vendor APIs (around 4000 RPM tier 4 for both, with higher tiers available).

Context Window Trade-off

Mini's 400K context is genuinely useful for RAG workloads with chunky retrieved context, codebases dropped into a prompt, or document analysis. Haiku's 200K window forces you to chunk earlier.

Both models degrade in needle-in-haystack performance past 100K tokens, but Mini's degradation curve is shallower in our testing. At 250K context with a single fact needle inserted at 60 percent depth, Mini retrieved with 94 percent accuracy; Haiku at 180K (max usable) retrieved with 89 percent.

If your retrieval pipeline produces 150K+ token contexts regularly, Mini has an architectural advantage. If you are working in the 20K to 80K range, both are fine.

Tool Use and Structured Output

This is where Haiku earns its premium. The benchmark gap (84.3% vs 81.7%) understates a more important behavioral difference: Haiku is significantly more reliable at staying inside a defined tool schema across long agent loops.

Specific failure modes we see more often with Mini:

• Filling optional parameters with hallucinated defaults instead of leaving them out
• Returning text alongside a tool call when the system prompt forbids it
• Choosing the wrong tool when two tools have overlapping descriptions

These are tractable problems with prompt engineering, but they show up as production noise. Haiku has tighter schema adherence by default, which translates to less defensive prompt engineering and fewer guardrail layers.

For structured JSON output without tools, both models support JSON mode and both are reliable. Anthropic's tool_choice: "any" and OpenAI's response_format: { type: "json_schema" } are both production-ready as of May 2026. Pick based on the ecosystem you are already in.

Multimodal

Haiku 4.5 ships vision and PDF input natively, same surface as Sonnet and Opus. Image throughput is faster than Sonnet 4.6 because the model is smaller.

GPT-5.5 Mini ships vision and audio input. The audio capability is differentiating: real-time voice agents on Mini are practical at price points that were not feasible six months ago.

For document-heavy commerce workflows (invoice extraction, receipt processing, product image analysis), both work. Audio agents push you toward Mini. Native PDF (vs image-based PDF) parsing tilts toward Haiku.

When to Use Which

Use Claude Haiku 4.5 when:

• Tool use accuracy is critical (multi-step agents, customer support automation, e-commerce assistants)
• You need PDF-native input
• You are already on Anthropic and the unit economics of Haiku are acceptable
• The workload has stable system prompts that benefit from prompt caching at scale
• Output quality matters more than output speed

Use GPT-5.5 Mini when:

• Cost dominates the decision (volume classification, high-volume extraction, batch inference)
• You need 250K+ context windows
• Latency-sensitive interactive workloads where TTFT and output speed matter
• You are building voice agents (audio input support)
• The workload is robust to occasional tool-use slop

Many production setups use both. Haiku for the agent control loop where tool fidelity matters; Mini for the bulk classification stage that processes data before it gets to the agent.

How to Evaluate This for Your Team

Run an A/B at your actual workload shape for two weeks. Specifically measure:

1. Cost per successful task, not cost per call. A 3x cheaper model that fails 20 percent more often is not 3x cheaper.
2. P95 latency under your real traffic pattern, not synthetic single-call benchmarks.
3. Tool use error rate, broken out by error type. Schema violations, wrong tool selection, and hallucinated arguments fail differently in production.
4. Cache hit rate and effective cached cost. Both vendors price caching aggressively; both have different cache semantics.

The frontier model comparison story is well-covered. We dug into the Opus 4.8 vs GPT-5.5 head-to-head and the SWE-Bench leaderboard recently. Fast tier is less covered because vendors do not spend marketing dollars there, but it is where most production cost actually lives.

If your stack is currently routing everything to a frontier model when 80 percent of the workload could run on Haiku or Mini, the savings are substantial. Contra Collective builds AI integration and agent infrastructure for teams trying to get unit economics right at scale. The right model tier per workload is usually the single biggest lever.

FAQ

Is Claude Haiku 4.5 actually a Haiku class model or rebadged Sonnet? Anthropic ships Haiku 4.5 as a smaller, faster model in the same family as the 4.x series. It is architecturally distinct from Sonnet 4.6. Benchmark deltas (Sonnet 4.6 scores 49.7% on SWE-Bench vs Haiku's 40.6%) confirm a real capability gap.

Does GPT-5.5 Mini support function calling and structured output? Yes. Both function calling and response_format: { type: "json_schema" } are fully supported. Reliability is high but slightly behind Haiku on adversarial tool selection tests.

What about Gemini Flash 2.5 in this comparison? Gemini Flash 2.5 sits in a similar tier. It is cheaper than both Haiku and Mini on input ($0.10 per 1M) but has weaker tool-use scores and a less mature agent ecosystem. Good for high-volume extraction; not our first choice for agent control loops.

Should I just use Opus 4.8 or GPT-5.5 and skip the fast tier entirely? Only if you do not care about cost. For most production workloads, 70 to 90 percent of calls can run on the fast tier without quality regression. Routing logic that escalates only when needed is usually a 4x to 8x cost reduction.

How stable is the pricing on these models? Both vendors have reduced fast-tier prices steadily through 2025 and 2026. Expect another reduction in the back half of 2026. Lock in pricing assumptions for budgeting, but plan for downward pressure on cost per token.

Does prompt caching work for both? Yes. Anthropic's prompt caching uses explicit cache_control markers and 5-minute TTL (with 1-hour cache extension available). OpenAI's prompt caching is automatic for repeated prefixes >1024 tokens with a 5 to 10 minute TTL. Both deliver 50 to 90 percent input cost reduction on cached portions.