All comparisons

Independent software comparison

Claude Opus 5.5 vs GPT-6 Astra

flat base pricing with deep caching discounts vs. tiered context pricing with multi-level reasoning controls

ai-models · high search interest

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 5.5

ai-models

GPT-6 Astra

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 Astra

Watch the comparison

Claude Opus 5.5 vs GPT-6 Astra: Pricing, Reasoning, and Workload Architecture

Expert analysis

Understanding the choice in practice

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

Specs at a glance

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.

CapabilityClaude Opus 5.5GPT-6 Astra
Starting priceFrom $4/1M input tokensFrom $10/1M input tokens
Free planNoNo
API availableProduct API availableProduct API available
Context window and output limit1M-token context, 128K-token output, 300K on the Batch API in beta1,050,000-token context, 128,000-token output
Thinking mode and effort controlAdaptive thinking always on; default effort mediumFive reasoning-effort levels, low through max
Tool use and agent supportTool use, with breaking changes from Opus 5Built for reasoning, coding, computer use, research and documents
Image and document inputText and image input, text outputText in and out, image in only; no audio or video
Long-horizon and autonomous workBuilt for long-running agentic coding and knowledge workComputer use and hosted shell for unattended work
Speed and latencyModerate latency, 30% faster output than Opus 5, fast mode up to 2.5xFlex and Batch as slower tiers; no fast mode under EU data residency
Caching, batch and speed pricingCache reads at 5% of input, Batch at half, fast mode at doubleCached input at a tenth of standard; Batch and Flex at half
Open weights and licence termsNo published weights; API access onlyNo published weights; API access only
Running it on your own hardwareHosted only; no self-hosted deployment offeredHosted only; no self-hosted deployment offered
Cloud platform availabilityClaude API, Bedrock, Google Cloud (both), Foundry and Claude Platform on AWSChat Completions, Responses and Batch; Tier 5 limits published
Availability in the vendor's own appsAvailable in Claude Code, including fast modeNot documented
Safeguards and refusal handlingFable 5.1-class safeguards; most cybersecurity tasks re-routedFirst model at the Critical cybersecurity level under the Preparedness Framework
Knowledge cutoff and retirement dateReleased 22 September 2026; cutoff June 2026; retirement not before 22 September 2027Knowledge cutoff 30 April 2026

Model benchmarks

Claude Opus 5.5 vs GPT-6 Astra on the benchmarks people cite

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.

Claude Opus 5.5 vs GPT-6 Astra across 7 benchmarksClaude Opus 5.5 vs GPT-6 Astra across 7 benchmarks.Claude Opus 5.5 vs GPT-6 Astra across 7 benchmarksClaude Opus 5.5GPT-6 Astra0%25%50%75%100%–GPT-6 Astra · FrontierMath Tier 4 · 97.6%98FrontierMathTier 4–GPT-6 Astra · FrontierMath Tiers 1–3 · 93.7%94FrontierMathTiers 1–3–GPT-6 Astra · ARC-AGI-2 · 95.0%95†ARC-AGI-2–GPT-6 Astra · Terminal-Bench 2.1 · 89.9%90Terminal-Bench2.1–GPT-6 Astra · DeepSWE · 74.1%74DeepSWEClaude Opus 5.5 · Humanity's Last Exam · 61.4%61GPT-6 Astra · Humanity's Last Exam · 54.7%55Humanity'sLast Exam–GPT-6 Astra · Artificial Analysis Coding Index · 77.1%77ArtificialAnalysis…Source: Epoch AI, 'AI Benchmarking Hub'. Published online at epoch.ai. Retrieved from'https://epoch.ai/benchmarks' [online resource]. Licence CC BY 4.0. Retrieved 2026-09-10.Source: Artificial Analysis, https://artificialanalysis.ai/ Licence Free API, attribution required. Retrieved2026-09-10.Source: Datacurve, DeepSWE leaderboard v1.1, https://deepswe.datacurve.ai/. Licence not stated (publicleaderboard, cited with attribution). Retrieved 2026-09-10.† Relayed by the source from a vendor or external leaderboard rather than run by it.TerraNet Technologies · terranettechnologies.com
Claude Opus 5.5 vs GPT-6 Astra across 7 benchmarks.
BenchmarkClaude Opus 5.5GPT-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 Exam61.4% at max54.7% 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

Where the differences matter

Context Windows and Cost Architecture

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.

Reasoning Control and Latency Profiles

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.

Tool Execution and Agent Integrations

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.

Safety Constraints and Infrastructure Footprint

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.

Claude Opus 5.5: pros and cons

What works

  • Anthropic's own models overview says to start with Opus 5.5 for most workloads, and the announcement puts it at the level of Fable 5.1 on most work.Models overview (Markdown), Claude docs · Introducing Claude Opus 5.5, Anthropic
  • At $4 in and $20 out, with cache reads at $0.20, it is 20% cheaper than Opus 5 per token and 60% cheaper on cache reads, which the announcement says make up the majority of agentic and coding costs.Claude API pricing · Introducing Claude Opus 5.5, Anthropic
  • The full 1M-token context and 128K-token output of Fable 5.1 at less than half of Fable 5.1's $10 and $50 rates.Claude Opus 5.5 model page, Claude docs

Tradeoffs

  • Four breaking changes for code written against Opus 5, including thinking that cannot be disabled and forced tool use that returns an error.Claude Opus 5.5 model page, Claude docs
  • Most cybersecurity tasks are re-routed to Opus 4.8 by its safeguards; the announcement says the Cyber Verification Program will be expanded to include Opus 5.5 soon, not that it already is.Introducing Claude Opus 5.5, Anthropic
  • Moderate latency in Anthropic's own comparison, behind Sonnet 5.5 and Haiku 5.5, with the faster option priced at double.Claude Opus 5.5 model page, Claude docs · Claude API pricing

GPT-6 Astra: pros and cons

What works

  • A 1,050,000-token context window with a 128,000-token output.GPT-6 Astra model page, OpenAI API docs
  • Five reasoning-effort levels, from low through xhigh to max, set per request.GPT-6 Astra model page, OpenAI API docs
  • Computer use is named on the model page as one of the things the model is for, rather than being left to the reader to infer from a tool list.GPT-6 Astra model page, OpenAI API docs

Tradeoffs

  • Requests above the short-context threshold are priced from a separate column - $20.00 in and $75.00 out - so a single long prompt re-prices everything around it.OpenAI API pricing
  • The first model OpenAI has classified at the Critical cybersecurity level, which brings trust-based access programmes and misalignment monitoring on all tool-using inference.GPT-6 Astra system card, OpenAI Deployment Safety Hub
  • No weights and no self-hosted path, and fast mode is unavailable under EU data residency.GPT-6 Astra model page, OpenAI API docs · OpenAI API pricing

Decision framework

How to choose between Claude Opus 5.5 and GPT-6 Astra

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

Our verdict

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

Evidence and editorial reviewed

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.

Editorial validation

Human-approved

Approved 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

Claude Opus 5.5 vs GPT-6 Astra FAQ

Can adaptive thinking be disabled in Claude Opus 5.5?

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.

How does prompt caching pricing compare between Opus 5.5 and GPT-6 Astra?

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).

What built-in tools are documented for GPT-6 Astra?

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.

How do Claude Opus 5.5 and GPT-6 Astra restrict cybersecurity workloads?

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

Related comparisons and alternatives

Claude Opus 5 Alternatives and Options

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 guide

GPT-6 Astra alternatives: models for long-horizon agentic workloads

GPT-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 guide

Claude Haiku 5.5 vs Claude Sonnet 5.5

Claude 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 guide

Claude Opus 5.5 vs Claude Fable 5.1

Claude 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 guide

Claude Sonnet 5.5 vs Claude Opus 5.5

Claude 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 guide

GLM-5.3-Flash vs Kimi K3

GLM-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