Artificial Analysis measures Claude Sonnet 5.5 at #2 intelligence with the heaviest token use it has ever recorded (~193k output tokens per task, ~7x GPT-6 Astra max), putting per-task cost ~50% above Sonnet 5 at unchanged Sol-matching pricing; AA's re-runs after the structured-output fix, and Anthropic's pricing or effort-setting response, resolve whether Sonnet 5.5's capability is economically viable for agent workloads.

Open article · Retrieved 2026-09-29T20:53:24.781221+00:00

[Artificial Analysis](https://artificialanalysis.ai/)

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September 28, 2026

# Anthropic has launched Claude Sonnet 5.5: it scores 56 on the Artificial Analysis Intelligence Index, just 2 points behind Opus 5.5 (max), but at the highest Output Tokens per Task we've seen

[See model page](https://artificialanalysis.ai/models/claude-sonnet-5-5)

With max effort, Sonnet 5.5 gains 18 points over Sonnet 5 and moves to #2 on the Intelligence Index, behind only Opus 5.5 (max). Anthropic has priced Sonnet 5.5 identically to Sonnet 5 at $0.2/$2/$10 per 1M cache input/input/output tokens. However, it outputs a higher number of Output Tokens per Task and costs $7.60 per task (~50% higher than Sonnet 5's Cost per Task).

**Key takeaways:**

➤ **Meets leading models on agentic terminal use and knowledge work:** In Terminal-Bench 4.0, Claude Sonnet 5.5 reaches 64% against 60% for Opus 5.5 and GPT-6 Astra. On AA-Briefcase (1811 vs 1822 Elo), GDPval-AA (1844 vs 1846 Elo), and AutomationBench-AA (71% vs 70% headline score), Sonnet 5.5 reaches parity with Opus 5.5, albeit with significantly higher token usage to achieve it

➤ **Heaviest token use we have measured:** At max effort, where it reaches performance nearing that of Opus 5.5, Claude Sonnet 5.5 used ~193k Output Tokens per Intelligence Index Task. This is the highest token use we have measured, around 60% higher than Opus 5.5 (max) or Sonnet 5 (max) and ~7x GPT-6 Astra (max)

➤ **Pricing remains at $2/$10 per million tokens of input/output, matching GPT-6 Sol:** At this pricing Claude Sonnet 5.5 sits off the Intelligence vs. Cost per Task Pareto Frontier. At high effort levels it sits behind Opus 5.5, while lower efforts have GPT-6 Astra or Sol configurations delivering equivalent performance for lower cost. The high effort setting is the most competitive on this basis, sitting very narrowly behind GPT-6 Sol on Intelligence at effectively the same Cost per Task

➤ **Behind Opus 5.5 on factual knowledge and scientific reasoning:** As a smaller class model, Sonnet 5.5 still lags on factual knowledge in AA-Omniscience compared to Opus 5.5. It scores 54% against 66% for factual accuracy, though with a lower hallucination rate (47% against 59%). It also sits ~6 points lower on Humanity's Last Exam and SciCode compared to Opus

These evaluations were conducted on a pre-release deployment of Claude Sonnet 5.5, which Anthropic found to have a bug that can degrade responses to requests that use structured outputs. This is fixed for the public release and Anthropic expects minimal change or slightly understated performance, but we will be re-running relevant evaluations soon.

**Other model details:**

➤ **Context window:** 1 million tokens with image and text input, unchanged from Sonnet 5

➤ **Pricing:** Unchanged from Sonnet 5's latest $2/$10 per 1M input/output tokens; cache writes at $2.5, cache reads $0.2

➤ **Effort settings:** Five (low, medium, high, xhigh, max). Intelligence Index evaluations were run at all five with Anthropic's default fallback enabled. We see Sonnet 5.5 fall back in ~0.1% of tasks across the Intelligence Index, primarily in Terminal-Bench 4.0, falling back to Sonnet 5 in all cases

Claude Sonnet 5.5 (max) makes large strides on Terminal-Bench, sitting among the top models for both Terminal-Bench 4.0 and Terminal-Bench-Science. In Terminal-Bench 4.0 it scores 64%, a 50 point increase over Claude Sonnet 5 (max), and slightly above 60% for Opus 5.5 and GPT-6 Astra (xhigh).

On our leaderboard for Terminal-Bench-Science, a benchmark of agentic terminal use to complete realistic scientific research workflows across domains, it scores 53% and sits behind only GPT-6 Astra and Opus 5.5. Terminal-Bench-Science is not currently included in the Artificial Analysis Intelligence Index.

To achieve its outsized performance, Claude Sonnet 5.5 (max) uses ~193k output tokens per Intelligence Index task, the most we have measured and ~7x that of GPT-6 Astra (max).

However, its lower effort settings span a broader area of Intelligence versus Output Tokens per Task tradeoffs, with low, medium, and high effort settings sitting behind GPT-6 Sol high, xhigh, and max efforts. In this area Sol provides higher performance with fewer output tokens.

Full breakdown of the individual evaluations in the Artificial Analysis Intelligence Index for Claude Sonnet 5.5 across all reasoning efforts:

Compare Claude Sonnet 5.5 with other leading models at: <https://artificialanalysis.ai/models/releases/claude-sonnet-5-5>

#### Read the latest

[### GPT-6.1 Sol replaces GPT-6 Sol after just 7 days, with near-Astra intelligence

GPT-6.1 Sol replaces GPT-6 Sol after just 7 days. It scores 1 point below GPT-6 Astra in the Intelligence Index at less than one quarter of the Cost per Task

September 29, 2026](https://artificialanalysis.ai/articles/gpt-6-1-sol-replaces-gpt-6-sol-after-just-7-days-with-near-astra-intelligence)[### Announcing the Artificial Analysis Cyber Index Alliance

The Artificial Analysis Cyber Index Alliance brings together industry partners to set a new standard for evaluating how AI models perform on enterprise cyber defense tasks. The Alliance launches alongside the Artificial Analysis Cyber Index, which combines three partner-contributed and open benchmarks to evaluate how well agents find and fix vulnerabilities.

September 28, 2026](https://artificialanalysis.ai/articles/artificial-analysis-cyber-index)[### GPT-6 Sol and Luna push the cost efficiency frontier

GPT-6 Sol and Luna push the cost efficiency frontier by halving cost relative to GPT-5.6 Sol and Luna. Intelligence Index and Coding Agent Index scores remain level with GPT-5.6, with progress in some evaluations and regressions in others

September 22, 2026](https://artificialanalysis.ai/articles/gpt-6-sol-and-luna-push-the-cost-efficiency-frontier)

Open article · Retrieved 2026-10-07T18:33:06.890057+00:00

Introducing Claude Haiku 5.5: the cheapest, fastest, and most capable small model we’ve ever released.

Claude Haiku 5.5 is designed for high-volume, cost-sensitive tasks. It reliably handles quick and repetitive workloads (like summaries, compactions, database queries, and classification requests). It pairs well with Opus 5.5 and Sonnet 5.5 as a subagent on coding work. And, since it’s also our fastest model to date, it works especially well for speed-sensitive tasks like live customer support and browser use.¹

Haiku 5.5 is available at a much lower price than Haiku 4.5. On average, it now costs around 75% less to run.²

Along with this launch, we’re making improvements to the value of our model range. We’re halving the price of Claude Sonnet 5.5’s cache reads, which means Sonnet 5.5 now runs around 20% cheaper on most agentic work. And we’re introducing a new monthly API credit for our Claude Max and Team subscribers, designed to support our users in building new agents and applications that run on the Claude Platform.

## Performance

Here’s how Claude Haiku 5.5 performs across a range of benchmarks:

|  | Haiku 5.5 | Haiku 4.5 | GPT-6 Luna |  | Sonnet 5.5For reference |
| --- | --- | --- | --- | --- | --- |
| Knowledge workGDPval-AA v2.1 |  | | | |
| --- | --- | --- | --- | --- |
| Knowledge workGDPval-AA v2.1 | 1620 | 735 | 1437 |  | 1840 |
| Knowledge workAA-Briefcase v1.1 |  | | | |
| Knowledge workAA-Briefcase v1.1 | 1578 | 614 | 1336 |  | 1824 |
| Computer useOSWorld 2.1 |  | | | |
| Computer useOSWorld 2.1 | 72.4%Offline subset | 15.7%Offline subset | 48.9%Offline subset |  | 83.9%Offline subset |
| Multidisciplinary reasoningHumanity’s Last Exam |  | | | |
| Multidisciplinary reasoningHumanity’s Last Exam | 45.9%no tools | 10.2%no tools | — |  | 56.9%no tools |
| 57.4%with tools | 18.7%with tools | — |  | 64.5%with tools |
| Agentic codingTerminal-Bench 4.0 |  | | | |
| Agentic codingTerminal-Bench 4.0 | 39.2% | 0.0% | 16.4% |  | 70.6% |
| Agentic codingFrontierCode 1.1 (Main) |  | | | |
| Agentic codingFrontierCode 1.1 (Main) | 46.4% | — | 42.4% |  | 52.1%Xhigh |
| Visual reasoningChartography |  | | | |
| Visual reasoningChartography | 46.4%no tools | 6.4%no tools | 29.1%no tools |  | 61.6%no tools |

For details on how we run our evaluations, see the [Haiku 5.5 System Card](https://www.anthropic.com/claude-haiku-5-5-system-card).

Haiku 5.5 is our first Haiku-class model to come with an adjustable effort setting. This means that, as with our other models, users can decide whether to optimize for cost or intelligence. The charts below show how Haiku 5.5 performs on three benchmarks at each effort setting:

Computer use: OSWorldKnowledge work: GDPval-AAMultidisciplinary reasoning: Humanity’s Last Exam

Computer use: OSWorldKnowledge work: GDPval-AAMultidisciplinary reasoning: Humanity’s Last Exam

OSWorld 2.1 (offline subset)Accuracy vs. cost

- **Haiku 5.5**
- **Haiku 4.5**
- **Sonnet 5.5**
- **GPT-6 Luna**

0102030405060708090Partial-credit score (%)0.050.100.200.50125Cost per attempt (USD, log scale)LowMedHighXhighMax

OSWorld 2.1 measures how well agents can operate a real computer to finish long, multi-step tasks.

GDPval-AA v2.1Accuracy vs. cost

- **Haiku 5.5**
- **Haiku 4.5**
- **Sonnet 5.5**
- **GPT-6 Luna**

8001000120014001600180020000Elo, as reported0.0050.010.020.050.100.200.50125Cost per task (USD, log scale)LowMedHighXhighMax

Artificial Analysis’s GDPval-AA v2.1 evaluates agents on real-world professional work across 44 occupations.

Humanity’s Last Exam (no tools)Accuracy vs. cost

- **Haiku 5.5**
- **Haiku 4.5**
- **Sonnet 5.5**

010203040506070Score (%)0.0050.010.020.050.100.200.501Cost per attempt (USD, log scale)LowMedHighXhighMax

Humanity’s Last Exam (HLE) is a test of expert-level academic knowledge and reasoning.

In early testing, our customers reported results consistent with the performance and cost improvements shown above. Here’s what they told us about the new model:

AsanaHubSpotAlphaSenseBoxRogoCognition

AsanaHubSpotAlphaSenseBoxRogoCognition

Quote
> “We’re very impressed with Claude Haiku 5.5, particularly its speed. We ran it through our eval suite for AI Teammates, our AI agent product, covering use cases like triaging bugs, setting up projects, and searching large portfolios to surface high-risk or overdue work. Compared with the model we use today, we saw over a 30% reduction in latency for task completions and up to 2.5x faster inference per agent turn. It’s a noticeably snappier experience.”

CompanyAsana

AuthorAaron Vinh, Staff Software Engineer

Quote
> “At HubSpot, we use simulated portals to evaluate new models on CRM tasks like reporting on deals. We mostly test the smaller, more efficient models, and Claude Haiku 5.5 got the best score we’ve seen on this suite yet, at 92.8% averaged over three runs. One CRM audit task asks models to identify stale but ambiguous records. Across all of the models we tested, Haiku 5.5 was fastest to complete the task, and had the highest hit rate and the lowest false positive rate.”

CompanyHubSpot

AuthorZe’ev Klapow, Distinguished Software Engineer

Quote
> “Ask in Document is one of our big sources of spend, doing about 8M calls a week in production. It answers very specific questions on top of one or a few documents. We ran 400 queries, and Claude Haiku 5.5 was a statistically significant improvement over Haiku 4.5: 0.84 vs. 0.76.”

CompanyAlphaSense

AuthorDaniel Campos, Distinguished Engineer

Quote
> “Our customers use Box AI across large volumes of their enterprise content. With widespread usage comes the need to manage efficiency and cost, and to find the best model to suit the task at hand. In early testing, Claude Haiku 5.5 scored 11 points higher than Haiku 4.5 at about half the latency. We’d put it to use on analytical work that runs at scale, from cost reports to financial summaries and weekly recurring reviews.”

CompanyBox

AuthorYashodha Bhavnani, VP of AI Products

Quote
> “The short and high-volume work is where Claude Haiku 5.5 fits for us, like quick lookups, subagents, and summaries. While a bigger model builds the deck, a Haiku 5.5 subagent goes into the 10-K and pulls the segment revenue line the deck needs. It’s accurate enough that we’d trust it there, and fast and cheap enough that we can run it a lot.”

CompanyRogo

AuthorAlex Wang, Applied AI

Quote
> “Claude Haiku 5.5 joins the sidekick lineup in Devin Fusion as an excellent option. With Haiku 5.5 as the sidekick, Fusion holds a top-tier FrontierCode score of 66.2 while cutting cost and latency. You can try it today in the Devin CLI with Opus 5.5 as the lead.”

CompanyCognition

AuthorWalden Yan, Co-Founder & CPO

## Pricing

The table below shows how Claude Haiku 5.5’s pricing compares to our other models. Haiku 5.5 is especially good value when used for tasks with prompts up to 100,000 tokens, which make up around 90% of requests to our previous Haiku model.

| Price per 1 million tokens | **Haiku 5.5** prompts up to / over 100k | **Haiku 4.5** | **Sonnet 5.5** |
| --- | --- | --- | --- |
| Cache reads | $0.01 / $0.05 | $0.10 | $0.10 |
| Cache writes | $0.125 / $0.625 | $1.25 | $2.50 |
| Input tokens | $0.10 / $0.50 | $1.00 | $2.00 |
| Output tokens | $0.50 / $2.50 | $5.00 | $10.00 |

## Safety

**Alignment.** Claude Haiku 5.5 shows major improvements across almost all of our alignment evaluations relative to Haiku 4.5. In particular, we found far fewer instances of misaligned behavior, and a lower willingness to cooperate with misuse. The model’s [system card](https://www.anthropic.com/claude-haiku-5-5-system-card) describes our evaluation process and results in more detail.

**Safeguards.** Consistent with its capabilities, Haiku 5.5’s cybersecurity safeguards are more restrictive than Haiku 4.5’s, but somewhat less restrictive than those we’ve applied to other recent models. In cybersecurity, they permit a wider range of defensive tasks than our safeguards for Sonnet 5.5, but they still block penetration testing and other techniques more likely to be used by attackers.

Haiku 5.5’s biology safeguards are the same as for Sonnet 5, Sonnet 5.5, and Opus 5. They allow research biology questions but restrict access to requests that we judge as likely to cause harm. Organizations working on wider-ranging biology and cyber activities can apply to our [Life Sciences Verification Program](https://www.anthropic.com/news/life-sciences-verification-program) and [Cyber Verification Program](https://www.anthropic.com/news/cyber-verification-program).

## Availability

Claude Haiku 5.5 is available now on all platforms, including Amazon Web Services, Google Cloud, and Microsoft Azure. On the Claude Platform, developers can get started with `claude-haiku-5-5`.

See our [migration guide](https://platform.claude.com/docs/en/models/haiku-5-5/migration-guide) for details.

## Further updates

Alongside our new pricing for Claude Haiku 5.5, we’re making further improvements to the value of our models and products.

First, starting today, we’re **lowering the price of cache reads on Claude Sonnet 5.5**. Cache reads now cost 50% less: $0.10 per million tokens rather than $0.20. Because cache reads make up a large share of models’ token consumption, this reduces the cost of Sonnet 5.5 on most agentic tasks by around 20%.

For instance, here’s what the price cut means for Sonnet 5.5’s performance relative to cost on Terminal-Bench 4.0:

Terminal-Bench 4.0Accuracy vs. cost

- **Haiku 5.5**
- **Haiku 4.5**
- **Sonnet 5.5** ($0.10 cache reads)
- **Sonnet 5.5** ($0.20 cache reads)

010203040506070Score (pass@1, %)0.5012510Cost per attempt (USD, log scale)LowMedHighXhighMax

Terminal-Bench 4.0 measures how well a model can complete complex, multi-step professional tasks within a command-line interface.

This chart illustrates an important difference between Haiku 5.5 and our larger models. Sonnet 5.5 and Opus 5.5 remain better choices for complex agentic coding tasks like those measured by Terminal-Bench 4.0. By contrast, Haiku 5.5 is best suited to more narrowly scoped tasks that might otherwise have been cost-prohibitive with previous versions of Claude—like compaction, summarization, or subagent work.

Second, this week, we’ll roll out **a new monthly API credit to all Max and Team subscribers for use on the Claude Platform**. Max 5x users will get $100 in credits per month, Max 20x users will get $200, and Team subscribers will receive up to $500, pooled across their users. These credits are designed to allow our users to experiment with building tools, apps, and agents that call our API. They can be used on any of our models. For more information, [see our Help Center article](https://support.claude.com/en/articles/17154008).

For developers, we’re also **updating our Claude Python and TypeScript SDKs to add support for computer use and browser use** in beta. Haiku 5.5 is especially well-suited to these tasks, given its combination of speed, capability, and price. You can read more about this [in our Claude Platform docs](https://platform.claude.com/docs/en/agents-and-tools/tool-use/browser-use-sdk).

## Footnotes

1 Claude Haiku 5.5 is our fastest model to date at each model’s standard speed, although it runs less quickly than our Opus models in Fast Mode.

2 Claude Haiku 5.5 is priced 90% lower than Claude Haiku 4.5 for requests up to 100,000 tokens, and 50% lower for requests over 100,000 tokens. On Haiku 4.5, 90% of requests fell into the former category. This calculation also accounts for changes between Haiku 4.5 and Haiku 5.5 in how many tokens are used to complete a given piece of work: Haiku 5.5 has an updated tokenizer (similar to Sonnet 5.5’s and Opus 5.5’s), which means it uses slightly more tokens per task.