Claude Opus 5.5 vs GPT-6 Astra: Which is Better in 2026

TLDR Claude Opus 5.5 (released September 22, 2026 by Anthropic at $4/$20 per million tokens) and GPT-6 Astra (released September 3, 2026 by OpenAI at $10/$50) represent the current peak of production frontier models.

Claude Opus 5.5 dominates agentic coding benchmarks (Terminal-Bench 4.0: 66.4% vs Astra’s 57.9%) and knowledge work while costing roughly 60% less, with cache reads five times cheaper. GPT-6 Astra leads in advanced mathematics (FrontierMath Tier 4: 97.6%), abstract reasoning (ARC-AGI-3), scientific research agents, computer use (OSWorld), and cybersecurity capability. For most software engineering agents and high-volume knowledge work, Opus 5.5 offers superior price-performance. For frontier math, science, and desktop/browser automation, Astra holds the edge. Both are accessible via unified platforms such as CometAPI.

Key Takeaways

• Pricing gap is decisive: Opus 5.5 at $4 input / $20 output vs Astra at $10 / $50. Cache reads: $0.20 vs $1.00. Typical agentic workloads favor Opus 5.5 by a wide margin.
• Agentic coding winner: Claude Opus 5.5 leads Terminal-Bench 4.0 (66.4% vs 57.9%) and edges FrontierCode; real-world reports show higher efficiency and fewer tokens per task.
• Research & math winner: GPT-6 Astra saturates FrontierMath Tier 4 (97.6%) and leads Terminal-Bench-Science and abstract reasoning benchmarks.
• Computer use: Astra currently holds published OSWorld advantages and faster task completion times.
• Context & specs: Both offer ~1M-token context windows and up to 128K output. Astra has a long-context pricing cliff above 272K input tokens; Opus 5.5 does not.
• Safety approaches differ: Opus 5.5 routes high-risk cyber requests to safer models; Astra is OpenAI’s first Critical-level cyber model with gated access for full capability.
• Practical recommendation: Default to Claude Opus 5.5 for coding agents and cost-sensitive production; switch to GPT-6 Astra for specialized scientific, mathematical, or heavy computer-use workloads. Test both easily via CometAPI.

Claude Opus 5.5 vs GPT-6 Astra-Pro at a Glance

What Is Claude Opus 5.5?

Claude Opus 5.5 is the first model in Anthropic's Claude 5.5 family. Anthropic says it reaches Fable 5.1-level performance on much of its workload while reducing typical serving cost versus Opus 5. The official launch notes emphasize fewer tokens per task, faster generation, clearer communication, and stronger long-running agent behavior.

Its defining operational characteristics are adaptive thinking that cannot be disabled, a medium default effort level, a 1M-token context window, and unusually low cache-read pricing. These traits make it a strong candidate for repository-scale engineering and repeated agent workflows with stable context.

What Is GPT-6 Astra?

GPT-6 Astra is OpenAI's flagship GPT-6 model for complex reasoning, software engineering, research, computer use, and artifact creation. Its Codex integration can preserve notes across context windows and search earlier context when long sessions exceed a single window. OpenAI's launch article presents this end-to-end workflow design as a core part of the model.

Astra combines a 1.05M-token context window with configurable reasoning effort, broad Responses API tool support, and native computer-use positioning. Its higher token rates make output efficiency and long-context pricing especially important in production budgeting.

Methodology note: Feature names and tool integrations are not one-to-one. Context size alone does not establish equivalent long-context quality, latency, or cost.

Claude Opus 5.5 vs GPT-6 Astra: Performance

Anthropic's launch table includes GPT-6 Astra alongside Opus 5.5 on several agentic and knowledge-work evaluations. Opus 5.5 is higher on Terminal-Bench 4.0, FrontierCode v1.1 Main, GDPval-AA v2.1, and Humanity's Last Exam, while Astra is higher on AutomationBench and Terminal-Bench Science 0.1.

Test conditions: Anthropic reports most Opus 5.5 results at adaptive thinking and max effort. Terminal-Bench 4.0 uses Opus 5.5 at xhigh effort and Astra at high effort; several Astra values are taken from OpenAI or third-party reporting rather than rerun in the same lab. Important: These figures are provider-reported results and are not necessarily directly comparable. Effort settings, harnesses, safeguards, evaluation setups, and reporting sources differ across benchmarks.

Agentic Coding and Software Engineering Performance

This is the clearest win for Claude Opus 5.5.

Anthropic’s published results show:

• Terminal-Bench 4.0: 66.4% (Opus 5.5) vs 57.9% (Astra)
• FrontierCode v1.1 Main: 54.4% vs 53.3%
• CursorBench 4.0: 57.8% (Opus 5.5 leads published comparisons)

Real-world feedback reinforces the numbers. Early testers and customers report that Opus 5.5 completes complex coding tasks (including a 680,000-line migration) with fewer steps, fewer tokens, and higher reliability at lower effort settings. Bug-catching rates in code review were higher even at low thinking effort compared with Opus 5 at high effort.

GPT-6 Astra remains highly competitive on DeepSWE v1.1 (74.1%) and other long-horizon repository tasks, but the terminal and general agentic coding edge currently sits with Anthropic’s model — at a fraction of the cost.

Knowledge Work, Reasoning, Math, and Science

Here the picture splits.

Claude Opus 5.5 strengths:

• Higher Elo on GDPval-AA knowledge-work evaluations
• Stronger Humanity’s Last Exam (with tools) scores
• Excellent multidisciplinary and professional knowledge work

GPT-6 Astra strengths:

• FrontierMath Tier 4 v2: 97.6% (near saturation)
• Leading Terminal-Bench-Science 0.1 (64.6% vs 58.7%)
• Dominant abstract reasoning results on ARC-AGI-3 (vendor harness 99.9%; independent standard harness lower but still strong)
• High scores on GPQA Diamond, BenchCAD, and scientific agent workflows

Astra is the preferred choice when the workload centers on advanced mathematics, formal scientific research pipelines, or novel abstract problem-solving. Opus 5.5 is stronger for broad professional knowledge work and multidisciplinary reasoning that mixes tools, documents, and coding.

Computer Use, Browser Automation, and Multimodal Workflows

GPT-6 Astra currently holds the published advantage on OSWorld 2.0 (approximately 72–73%) and related computer-use evaluations, with reported faster task completion times than its predecessor. It also shows strong ScreenSpot-Pro and browser-use results. Claude Opus 5.5 has solid computer-use capabilities and improved visual reasoning, but public head-to-head numbers favor Astra in pure desktop/browser automation scenarios

Independent Evaluation: Artificial Analysis

Artificial Analysis compares Claude Opus 5.5 at adaptive reasoning, max effort, default fallback against GPT-6 Astra at max effort. Its current comparison gives Opus 5.5 an Intelligence Index of 58 and Astra 53. Artificial Analysis aggregates multiple evaluations into its Intelligence Index, so the index should be treated as a composite evaluation rather than a single benchmark score.

The narrower 60% versus 59% Terminal-Bench result shows why benchmark margins should be treated as workload-selection evidence, not a universal ranking. Harnesses, effort settings, safeguards, fallback behavior, and stopping criteria can materially change the outcome.

Claude Opus 5.5 vs GPT-6 Astra: Cost

Official API pricing

At standard rates, Claude Opus 5.5 is cheaper per token. Anthropic charges $4 per million input tokens, $20 per million output tokens, $0.20 per million cache reads, $5 per million five-minute cache writes, and $8 per million one-hour cache writes. Fast mode costs $8 input and $40 output per million tokens.

OpenAI charges $10 per million input tokens, $50 per million output tokens, $1 per million cached input tokens, and $12.50 per million cache writes for Astra. Above 272K input tokens, the full request is billed at 2x input/cache rates and 1.5x output rates.

Opus 5.5 is approximately 2.5× cheaper on base rates and up to 5× cheaper on the cache reads that dominate long-running agentic sessions. Anthropic reports that typical workloads cost ~40% less than Claude Opus 5; the gap versus Astra is even larger for most production coding and knowledge pipelines. Astra’s long-context pricing cliff above 272K tokens further widens the difference for large-context agents.

Cost per completed task

Per-token pricing does not determine cost per completed workload. In the current Artificial Analysis max-effort comparison, Opus 5.5 uses 119K output tokens and 84K reasoning tokens per Intelligence Index task, while Astra uses 27K output tokens and 17K reasoning tokens. This produces an estimated $5.98 per task for Opus 5.5 versus $3.26 for Astra in that evaluation.

This does not make Astra universally cheaper. Cache-heavy coding agents may favor Opus 5.5, while workloads where Astra reaches the acceptance threshold with much less output can reverse the rate-card advantage.

CometAPI pricing

The Claude Opus 5.5 API in CometAPI uses a 20%-discounted base tier: $3.20 per million input tokens and $16 per million output tokens. Cache reads are $0.16 per million, with five-minute and one-hour cache writes at corresponding discounted rates.

The GPT-6 Astra API in CometAPI uses $8 per million input tokens and $40 per million output tokens for short-context requests. Its long-context tier mirrors OpenAI's multiplier structure at discounted rates: $16 input and $60 output per million tokens.

Context Windows, Speed, and Technical Specs

Both models offer roughly 1-million-token context windows and up to 128K output tokens. Knowledge cutoffs are close (Astra: April 30, 2026; Opus 5.5: June 2026). Opus 5.5 is reported to generate output more than 30% faster than its predecessor and often shows higher tokens-per-second in independent measurements. Astra supports multiple reasoning effort levels and has a Fast mode; Opus 5.5 uses always-on adaptive thinking (cannot be fully disabled) with effort controls.

Safety, Alignment, and Deployment Considerations

The two companies take different product approaches:

• Claude Opus 5.5: Strongest model yet on Anthropic’s automated behavioral audit. High-risk cybersecurity requests are re-routed to a safer model (Opus 4.8). Ships with Fable-class safeguards for biology and anti-distillation. Designed for broad production deployment from day one.
• GPT-6 Astra: OpenAI’s first model to reach Critical cybersecurity capability under its Preparedness Framework. Full offensive cyber capability is gated. Strong alignment improvements over GPT-5.6 Sol, but the higher raw capability requires additional access controls for the most powerful features.

Organizations with strict compliance or open deployment needs may prefer Opus 5.5’s containment strategy; those needing maximum cyber or research capability (under controlled access) may choose Astra.

Claude Opus 5.5 vs GPT-6 Astra: Which Should You Choose?

• Choose Claude Opus 5.5 if your primary workloads are software engineering agents, terminal automation, high-volume knowledge work, or any scenario where cost and reliability at scale matter most. It is currently the better default for most production agent systems.
• Choose GPT-6 Astra if you need state-of-the-art performance on advanced mathematics, scientific research agents, complex computer/browser use, or CAD/engineering synthesis, and budget allows the higher token rates.
• Hybrid approach: Many teams will route coding and general agents to Opus 5.5 and specialized research or computer-use tasks to Astra. CometAPI makes this trivial by exposing both models behind one API key and consistent interface.

Workload-based selection

Use this table as a test plan, not a universal ranking. Run the same prompt, context, tools, timeout, retry policy, and acceptance criteria against both models.

How to Access and Use Claude Opus 5.5 and GPT-6 Astra

Claude Opus 5.5 API in CometAPI supports Anthropic Messages and OpenAI-compatible Chat formats. Use the native Messages shape when you need Claude-specific controls and content blocks.

Python — Claude Opus 5.5 through the Anthropic SDK

import os
import anthropic

client = anthropic.Anthropic(
    api_key=os.environ["COMETAPI_KEY"],
    base_url="https://api.cometapi.com",
)

message = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=2048,
    messages=[{
        "role": "user",output_config={"effort": "medium"},
        "content": "Review this migration plan and list the highest-risk steps.",
    }],
)

print(message.content[0].text)

GPT-6 Astra API in CometAPI supports OpenAI-compatible routing. The Responses API is the natural starting point for tool-rich integrations.

Python — GPT-6 Astra through the OpenAI SDK

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["COMETAPI_KEY"],
    base_url="https://api.cometapi.com/v1",
)

response = client.responses.create(
    model="gpt-6-astra",    reasoning={"effort": "medium"},
    input="Review this migration plan and list the highest-risk steps.",
)

print(response.output_text)

For a fair internal evaluation, keep prompts and acceptance criteria identical, record the same tool-call budget and retry policy, and measure total billed tokens rather than only the first successful response.

Conclusion

Claude Opus 5.5 offers the lower rate card, stronger cache economics, and compelling evidence for long-running coding and professional knowledge work. GPT-6 Astra offers broader end-to-end tool positioning, strong scientific and computer-use results, and much lower token use in the current Artificial Analysis max-effort comparison.

Neither model is the universal winner. Teams dominated by stable cached context and repository-scale work should test Opus 5.5 first; teams dominated by scientific reasoning, computer interaction, or expensive output generation should test Astra first. The final decision should be based on cost per accepted result under a shared evaluation protocol.

CometAPI provides access to both model families through familiar SDK patterns, making it practical to run the same workload against both routes before selecting a production default.