ai-models
Claude Opus 5.5
Anthropic's recommended starting model, at Fable 5.1's level on most work for 40% less than Opus 5
Starts at
From $4/1M input tokens
Pricing tier: Usage-Based
Visit Claude Opus 5.5Independent software comparison
flat base pricing with deep caching discounts vs. tiered context pricing with multi-level reasoning controls
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ai-models
Anthropic's recommended starting model, at Fable 5.1's level on most work for 40% less than Opus 5
Starts at
From $4/1M input tokens
Pricing tier: Usage-Based
Visit Claude Opus 5.5ai-models
OpenAI's most capable model for the hardest end-to-end work, at $10 in and $50 out per million tokens
Starts at
From $10/1M input tokens
Pricing tier: Usage-Based
Visit GPT-6 AstraWatch the comparison
Expert analysis
A flat pricing floor of $4.00 per million input tokens across a full million-token window separates Claude Opus 5.5 from GPT-6 Astra, which shifts to a higher pricing tier once prompts exceed standard context thresholds. Both cloud-hosted models handle massive inputs to power long-running agentic workloads and complex software engineering tasks, yet they balance infrastructure expenses and execution control differently. Opus 5.5 combines uniform token billing with always-on adaptive thinking and steep prompt-caching discounts. GPT-6 Astra pairs its large context with a five-tier reasoning scale and native tools like containers and computer use, but long-context prompts cause input and cached input rates to double while output costs increase 1.5 times. Deciding between them comes down to whether your workflows prioritize predictable token economics across large prompts or fine-grained deliberation dials alongside hosted execution tools.
Feature matrix
Rows are grouped by capability, and each cell shows the wording from that vendor’s own documentation. “Not documented” means we found no cited source for that capability, which is not the same as the product lacking it.
| Capability | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|
| Starting price | From $4/1M input tokens | From $10/1M input tokens |
| Free plan | No | No |
| API available | Product API available | Product API available |
| Context window and output limit | 1M-token context, 128K-token output, 300K on the Batch API in beta | 1,050,000-token context, 128,000-token output |
| Thinking mode and effort control | Adaptive thinking always on; default effort medium | Five reasoning-effort levels, low through max |
| Tool use and agent support | Tool use, with breaking changes from Opus 5 | Built for reasoning, coding, computer use, research and documents |
| Image and document input | Text and image input, text output | Text in and out, image in only; no audio or video |
| Long-horizon and autonomous work | Built for long-running agentic coding and knowledge work | Computer use and hosted shell for unattended work |
| Speed and latency | Moderate latency, 30% faster output than Opus 5, fast mode up to 2.5x | Flex and Batch as slower tiers; no fast mode under EU data residency |
| Caching, batch and speed pricing | Cache reads at 5% of input, Batch at half, fast mode at double | Cached input at a tenth of standard; Batch and Flex at half |
| Open weights and licence terms | No published weights; API access only | No published weights; API access only |
| Running it on your own hardware | Hosted only; no self-hosted deployment offered | Hosted only; no self-hosted deployment offered |
| Cloud platform availability | Claude API, Bedrock, Google Cloud (both), Foundry and Claude Platform on AWS | Chat Completions, Responses and Batch; Tier 5 limits published |
| Availability in the vendor's own apps | Available in Claude Code, including fast mode | Not documented |
| Safeguards and refusal handling | Fable 5.1-class safeguards; most cybersecurity tasks re-routed | First model at the Critical cybersecurity level under the Preparedness Framework |
| Knowledge cutoff and retirement date | Released 22 September 2026; cutoff June 2026; retirement not before 22 September 2027 | Knowledge cutoff 30 April 2026 |
Model benchmarks
Claude Opus 5.5 runs on Claude Opus 5.5 and GPT-6 Astra on GPT-6 Astra. These are the models’ scores, not the tools’: independent evaluations from Epoch AI, Artificial Analysis and Datacurve, each at the model’s best published effort setting, last read 2026-09-28. A dash means the model has not been scored on that benchmark yet.
| Benchmark | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|
| FrontierMath Tier 4 | – | 97.6% at high |
| FrontierMath Tiers 1–3 | – | 93.7% at max |
| ARC-AGI-2 | – | 95.0% † at max |
| Terminal-Bench 2.1 | – | 89.9% at high |
| DeepSWE | – | 74.1% at xhigh |
| Humanity's Last Exam | 61.4% at max | 54.7% at max |
| Artificial Analysis Coding Index | – | 77.1% at high |
Sources: Artificial Analysis · Epoch AI · Datacurve.
† Relayed by the source from a vendor or external leaderboard rather than run by it.
Detailed comparison
Both architectures accommodate massive inputs, with Claude Opus 5.5 offering a 1,000,000-token context window paired with 128,000 tokens of synchronous output (and up to 300,000 output tokens on its Message Batches API in beta). GPT-6 Astra provides a 1,050,000-token context window supporting up to 128,000 output tokens. Their accounting models diverge once workloads scale across large prompts. Anthropic bills Opus 5.5 at a flat $4.00 per million input tokens and $20.00 per million output tokens across the entire one-million-token span. Prompt caching on Opus 5.5 drops cache reads to $0.20 per million tokens, reflecting a 0.05x multiplier against baseline input, with 5-minute cache writes at $5.00 and 1-hour writes at $8.00. GPT-6 Astra splits billing into two distinct context columns. Standard short-context requests cost $10.00 per million input tokens and $50.00 per million output tokens, with cached input at $1.00 and cache writes at $12.50. When a prompt crosses OpenAI's short-context threshold, rates move to a separate column: $20.00 per million input tokens, $2.00 for cached reads, $25.00 for cache writes, and $75.00 per million output tokens. As a result, sustained million-token workflows that frequently consult massive prompt contexts carry substantially lower per-token overhead on Opus 5.5.
The two models implement contrasting mechanisms for controlling internal deliberation. Claude Opus 5.5 enforces adaptive thinking that remains permanently active and cannot be disabled. Anthropic sets the default effort parameter to medium, allowing developers to adjust thinking depth without turning it off entirely. The model page lists Opus 5.5's comparative latency as moderate, though Anthropic offers a fast mode delivering up to 2.5 times speed at double the base price ($8.00 input and $40.00 output). GPT-6 Astra introduces five explicit reasoning-effort levels: low, medium, high, xhigh, and max, configurable per request on the reasoning effort parameter. This allows teams to dial deliberation down to low for faster turns or increase it to max for intricate algorithmic work. While Astra supports Batch and Flex processing at 50% of standard rates, fast mode is explicitly unavailable for GPT-6 Astra under EU data residency rules.
Opus 5.5 introduces breaking API changes that constrain how tools are invoked, whereas Astra expands out-of-the-box automation through hosted runtimes and shell environments. Code running on Opus 5 faces four documented breaking changes when upgrading to Opus 5.5: thinking cannot be disabled, forced tool use returns an error, thinking blocks are bound to the model and conversation, and the earlier computer_20251124 tool identifier is no longer accepted on the Claude API and Google-managed Google Cloud. Text between tool calls also returns inside thinking blocks whose text is empty at default display settings. For GPT-6 Astra, OpenAI documents built-in tools including web search, file search, and container environments covering Hosted Shell and Code Interpreter, billed at the model's standard per-token rates. OpenAI explicitly lists computer use among the primary applications for Astra on its model page, alongside document creation and coding, with dedicated per-tool-call fees charged for search and computer use operations.
Neither model offers downloadable weights or self-hosted deployments; both are restricted to vendor-managed cloud APIs. Platform availability varies across hosting providers. Claude Opus 5.5 is available under the claude-opus-5-5 identifier on the Claude API, Microsoft Foundry, Claude Platform on AWS, and both Google-managed and Anthropic-managed Google Cloud, as well as anthropic.claude-opus-5-5 on Amazon Bedrock. Astra is accessible via OpenAI's Chat Completions, Responses, and Batch endpoints, publishing Tier 5 rate limits of 15,000 requests and 40,000,000 tokens per minute. Safety frameworks also set distinct boundaries. GPT-6 Astra is OpenAI's first model evaluated at the Critical cybersecurity level under its Preparedness Framework, enforcing continuous misalignment monitoring on all tool-using inference alongside trust-based access programs for high-risk capabilities. Opus 5.5 uses safeguards comparable to Claude Fable 5.1; it automatically re-routes most cybersecurity tasks to Opus 4.8 while keeping routine bug fixing in user code available, pending the expansion of Anthropic's Cyber Verification Program.
Best use case for Claude Opus 5.5
Engineering teams building sustained agentic coding workflows that heavily reuse large prompts and require flat token pricing across a full 1M-token context.
Best use case for GPT-6 Astra
Developers building end-to-end autonomous agents that need low-to-maximum reasoning controls, built-in shell tools, and native computer use.
Decision framework
Choose Claude Opus 5.5 if your application centers on sustained, iterative code editing, large codebase navigation, or expansive document indexing where token costs must remain strictly flat. Because Opus 5.5 charges a uniform $4.00 per million input tokens across its entire 1,000,000-token context window and discounts cached prompt reads to $0.20 per million tokens, it offers strong cost efficiency for iterative loops that repeatedly consult long prompt histories. It is also the appropriate choice if you deploy across multi-cloud infrastructure such as Amazon Bedrock or Google Cloud. Choose GPT-6 Astra if your architecture requires five-level reasoning controls spanning low to max effort, or if you depend on OpenAI's built-in tools such as Hosted Shell, Code Interpreter, and native computer use. Astra is well suited for autonomous workflows that benefit from managed containerized environments, provided your operational budget can accommodate long-context rates of $20.00 per million input tokens and $75.00 per million output tokens.
Bottom line
Opus 5.5 locks in predictable token economics at $4.00 per million input tokens across its full one-million-token window, whereas Astra charges higher base rates that escalate to $20.00 for long-context prompts in exchange for five levels of reasoning effort and pre-configured execution environments. For teams building sustained agentic coding loops that repeatedly query massive prompt caches, Opus 5.5 keeps operational spend low through $0.20 cache reads and broad availability across AWS Bedrock, Google Cloud, and Microsoft Foundry. Conversely, GPT-6 Astra is engineered for end-to-end autonomous runs that require manual tuning between low and maximum deliberation, bundled shell containers, and native computer use, making it the practical selection when operational autonomy takes precedence over raw token budget predictability.
Sources and verification
The product facts have been checked against the sources below. The AI-assisted analysis was audited against these exact evidence records and approved by a human editor.
Last verified October 25, 2026
Last verified September 21, 2026
Editorial validation
Human-approvedApproved October 28, 2026 after an automated evidence audit using claude-opus-5-5.
Read our comparison methodology and editorial policy, learn about TerraNet, or report a correction.
Common questions
No. In Claude Opus 5.5, adaptive thinking is always on and cannot be disabled through API parameters. Developers can adjust the thinking depth using the effort parameter, which defaults to medium, but requests cannot bypass deliberation entirely.
Claude Opus 5.5 prices cache reads at 0.05x its base input rate, charging $0.20 per million tokens, with 5-minute cache writes at $5.00 and 1-hour writes at $8.00. GPT-6 Astra bills cached input at a tenth of base rates, costing $1.00 per million tokens for standard requests (with $12.50 cache writes) and $2.00 per million tokens for long-context requests (with $25.00 cache writes).
OpenAI API pricing and model documentation list built-in tools for GPT-6 Astra covering web search, file search, and container environments including Hosted Shell and Code Interpreter, with computer use explicitly named as a core application on the model page.
Claude Opus 5.5 automatically redirects most cybersecurity tasks to Opus 4.8 via built-in safeguards, while routine bug fixing in user code remains permitted. GPT-6 Astra is classified at the Critical cybersecurity level under OpenAI's Preparedness Framework, which enforces continuous misalignment monitoring on tool-using inference and trust-based access programs for high-risk capabilities.
AI-assisted draft audited against the cited product evidence and approved by a human editor. Vendor pricing and capabilities can change after the recorded verification date.
Continue researching
In Claude Opus 5.5, adaptive thinking is always on and cannot be turned off. For engineering teams maintaining existing agent pipelines, this architectural shift introduces significant breaking changes: forced tool choice now returns an explicit API error, intermediate reasoning tokens are strictly bound to specific model conversation states, and the earlier computer_20251124 computer use tool is no longer accepted on the Claude API and Google-managed Google Cloud. While Opus 5.5 delivers high-tier reasoning at $4 per million input tokens and $20 per million output tokens, its baseline latency profile is categorized as Moderate, trailing faster alternatives unless buyers pay double the base price to invoke its fast mode. In addition, its automated safeguard mechanisms re-route the majority of cybersecurity tasks away from the model to Opus 4.8. These structural constraints, pricing multiples, and behavioral boundaries prompt technical buyers to evaluate adjacent models across Anthropic's own catalogue and competing developer ecosystems.
Read guideGPT-6 Astra's separate long-context billing tier doubles input costs to $20.00 and increases output costs to $75.00 per million tokens once prompts cross the short-context threshold, altering financial projections for extended agentic loops. While the model delivers an extensive 1,050,000-token context window, five discrete reasoning effort levels, and hosted agentic capabilities including computer use and Hosted Shell, its delivery profile introduces concrete architectural boundaries. The system distributes no downloadable weights, preventing self-hosted or air-gapped deployments, and it excludes fast mode when running under EU data residency requirements. Furthermore, because OpenAI designated Astra at the Critical cybersecurity capability tier under its Preparedness Framework, all external tool-using inference undergoes mandatory misalignment monitoring, with trust-based access programmes applied to high-risk capability areas. Teams building production systems frequently examine alternative foundation models when seeking more uniform long-context pricing structures, faster comparative response latencies, multi-cloud hosting availability, or native ingestion of non-image sensory modalities like audio and video.
Read guideClaude Haiku 5.5 carries Anthropic's Fastest latency rating at $0.10 per million input tokens for prompts up to 100,000 tokens, while Claude Sonnet 5.5 costs $2.00 but posts a reported 70.6 percent on Terminal-Bench 4.0. Teams managing massive transaction volumes, interactive user interfaces, quick classification, and secondary subagent tasks will find Haiku 5.5 the lower-cost, lower-latency option. Organizations developing autonomous coding agents, multi-step tool architectures, and complex software workflows should adopt Sonnet 5.5, accepting its higher baseline token expense and the five breaking changes for code written against Sonnet 5 to secure its high-effort reasoning and advanced tool orchestration.
Read guideClaude Opus 5.5 cuts baseline token pricing to $4 in and $20 out per million tokens with moderate latency, whereas Claude Fable 5.1 demands $10 in and $50 out alongside Anthropic's slowest latency tier to run high-effort deliberation on long-horizon workloads. For the vast majority of software development tasks, automated agent workflows, and general enterprise document analysis, Opus 5.5 serves as the recommended baseline starting point. Fable 5.1 functions as a targeted escalation tier, warranted when empirical evaluations show that Opus 5.5 at higher effort levels fails demanding reasoning benchmarks.
Read guideClaude Sonnet 5.5 delivers a high-speed execution engine at $2.00 per million input tokens, whereas Claude Opus 5.5 charges $4.00 per million input tokens to power an always-on deliberation model tuned for sustained autonomous workflows. Sonnet 5.5 provides clear advantages for standard production applications where throughput is critical and token volume accumulates rapidly, delivering a Fast comparative latency rating with output generation more than 30 percent faster than Sonnet 5, alongside flexible controls that let engineers turn off up-front thinking. Opus 5.5 justifies its double-rate token cost for complex, multi-file codebases and deep knowledge synthesis where Anthropic explicitly positions it as the starting model, backed by its own 30 percent output speedup over Opus 5. However, engineering teams must recognize that both models enforce cyber safeguards and fallback mechanisms, making neither model an unconstrained option for specialized security tooling. Balancing the two models often means routing routine API traffic and high-volume tasks through Sonnet 5.5 while escalating long-horizon autonomous tasks to Opus 5.5.
Read guideGLM-5.3-Flash delivers an accessible, lightweight 18B active-parameter architecture under an unencumbered MIT license with rock-bottom operational costs, whereas Kimi K3 demands a massive multi-node 2.8T-parameter footprint, custom legal approval, and always-on reasoning tokens to unlock frontier-level agentic task completion. For high-volume production, multimodal file pipelines, and internal deployments on standard server setups, GLM-5.3-Flash provides the most practical and legally clear path forward. For demanding terminal control, automated web browsing, and multi-step programmatic problem solving where accuracy supersedes operational cost, Kimi K3 stands as the superior agentic reasoning tool.
Read guide