Claude Haiku 5.5 vs GPT-5.1
Anthropic · US | OpenAI · US · Updated June 2026
Quick verdict
Pick Claude Haiku 5.5 for 5x the context window of haiku 4.5 (1m vs 200k tokens) and double the max output (128k vs 64k) or first haiku-class model with an adjustable 'effort' setting to trade off cost, latency, and intelligence. Pick GPT-5.1 for adaptive reasoning that scales compute to task difficulty or general-purpose reasoning, coding and tool use. On a tight budget at scale, Claude Haiku 5.5 is the value pick.
Claude Haiku 5.5 (Anthropic) and GPT-5.1 (OpenAI) are two of the models people most often weigh against each other in 2026. Claude Haiku 5.5 is anthropic's October 7, 2026 small-model refresh — a 1M-token context window (up from 200K) at roughly 75% lower average cost than Haiku 4.5, priced at $0.10/$0.50 per million tokens. GPT-5.1 is openAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Claude Haiku 5.5 is about 13× cheaper on input ($0.1/$0.5 per 1M tokens vs $1.25/$10 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: Claude Haiku 5.5 holds 2.5× more — 1M tokens (~1,500 pages) vs 400K (~600 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Claude Haiku 5.5 is the newer model by about 11 months (released October 7, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Claude Haiku 5.5 | GPT-5.1 |
|---|---|---|
| Provider | Anthropic (US) | OpenAI (US) |
| Released | October 7, 2026 | November 13, 2025 |
| Context window | 1M tokens (~1,500 pages) | 400K (~600 pages) |
| Price (in/out) | $0.1/$0.5 per 1M tokens | $1.25/$10 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
5x the context window of Haiku 4.5 (1M vs 200K tokens) and double the max output (128K vs 64K)
Claude Haiku 5.5
Its 1M tokens window holds about 2.5× more than GPT-5.1's 400K in a single prompt.
First Haiku-class model with an adjustable 'effort' setting to trade off cost, latency, and intelligence
Claude Haiku 5.5
At $0.1/$0.5 per 1M tokens it undercuts GPT-5.1 ($1.25/$10 per 1M tokens), and that gap compounds at volume.
Anthropic's cheapest small model yet — runs about 75% cheaper on average than Haiku 4.5
Claude Haiku 5.5
GPT-5.1 is comparatively weak here — premium output price at $10/1M for a now-older model
Adaptive reasoning that scales compute to task difficulty
GPT-5.1
Claude Haiku 5.5 is comparatively weak here — as the smallest tier, trails both Sonnet 5.5 and Opus 5.5 on hard reasoning and open-ended work
General-purpose reasoning, coding and tool use
GPT-5.1
GPT-5.1 lists general-purpose reasoning, coding and tool use among its strengths; Claude Haiku 5.5 does not.
A large ~400K context for long documents and sessions
GPT-5.1
GPT-5.1 lists a large ~400K context for long documents and sessions among its strengths; Claude Haiku 5.5 does not.
Lowest cost at scale
Claude Haiku 5.5
At $0.1/$0.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Claude Haiku 5.5
Its 1M tokens window is about 2.5× larger than GPT-5.1's 400K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Claude Haiku 5.5
At $0.1/$0.5 per 1M tokens it undercuts GPT-5.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Claude Haiku 5.5
Larger 1M tokens window fits more in one prompt.
Anyone whose priority is 5x the context window of haiku 4.5 (1m vs 200k tokens) and double the max output (128k vs 64k)
→ Claude Haiku 5.5
It is specifically built for that.
Anyone whose priority is adaptive reasoning that scales compute to task difficulty
→ GPT-5.1
That is its strongest area.
Claude Haiku 5.5: where it fits
Anthropic's October 7, 2026 small-model refresh — a 1M-token context window (up from 200K) at roughly 75% lower average cost than Haiku 4.5, priced at $0.10/$0.50 per million tokens. Released October 7, 2026 by Anthropic, it is built for 5x the context window of Haiku 4.5 (1M vs 200K tokens) and double the max output (128K vs 64K), first Haiku-class model with an adjustable 'effort' setting to trade off cost, latency, and intelligence, anthropic's cheapest small model yet — runs about 75% cheaper on average than Haiku 4.5, and over-refuses benign requests only 0.17% of the time on the API, down from Haiku 4.5's 0.44%.
Its trade-offs are real: pricing jumps roughly 5x beyond 100K-token prompts ($0.50/$2.50 per million vs the headline $0.10/$0.50), extended thinking can no longer be fully disabled — only dialed between low, medium, and high effort, and as the smallest tier, trails both Sonnet 5.5 and Opus 5.5 on hard reasoning and open-ended work. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
GPT-5.1: where it fits
OpenAI's late-2025 GPT-5.1 at $1.25/$10 — a capable general model since surpassed by the GPT-5.2–5.6 line. Released November 13, 2025 by OpenAI, it is built for adaptive reasoning that scales compute to task difficulty, general-purpose reasoning, coding and tool use, a large ~400K context for long documents and sessions, and text and image input.
Its trade-offs: superseded by GPT-5.2 through GPT-5.6 — kept for compatibility rather than being current, no reliably published SWE-Bench Verified figure, exact context window unconfirmed (400K vs 410K across trackers), and premium output price at $10/1M for a now-older model. At $1.25 in / $10 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
Claude Haiku 5.5 and GPT-5.1 overlap enough that the right pick depends on your specific job. Claude Haiku 5.5 costs less per token; Claude Haiku 5.5 holds the larger context; and each leads in its own area — Claude Haiku 5.5 for 5x the context window of haiku 4.5 (1m vs 200k tokens) and double the max output (128k vs 64k), GPT-5.1 for adaptive reasoning that scales compute to task difficulty. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Claude Haiku 5.5 and GPT-5.1 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is Claude Haiku 5.5 or GPT-5.1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Claude Haiku 5.5 leans toward 5x the context window of haiku 4.5 (1m vs 200k tokens) and double the max output (128k vs 64k) while GPT-5.1 leans toward adaptive reasoning that scales compute to task difficulty, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Claude Haiku 5.5 or GPT-5.1?
Claude Haiku 5.5 is cheaper — $0.1/$0.5 per 1M tokens vs $1.25/$10 per 1M tokens, roughly 13× apart on input.
Which has the bigger context window?
Claude Haiku 5.5 — 1M tokens vs 400K, about 2.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Claude Haiku 5.5 and GPT-5.1 together?
Yes — a multi-model platform like LumiChats gives you Claude Haiku 5.5, GPT-5.1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Claude Haiku 5.5 or GPT-5.1?
Claude Haiku 5.5 — released October 7, 2026, about 11 months after GPT-5.1.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.