GPT-6 Astra vs Claude 5.5: which model earns the next task?

Claude Opus 5.5 is a strong first candidate for difficult autonomous coding and document-heavy work; GPT-6.1 Sol is an attractive default when repeated calls make cost matter. Astra deserves a separate look for science and hard reasoning, while Sonnet 5.5 is particularly competitive in interface-building tests. Those are our interpretations of the evidence below, rather than results of our own model trial.

We compare four specific models: GPT-6 Astra, GPT-6.1 Sol, Claude Opus 5.5 and Claude Sonnet 5.5. This is a comparison of the requested families as of October 24, 2026, not an exhaustive ranking of every current AI model. We reviewed primary sources and assembled 69 metrics with four model slots each; missing scores remain empty. We did not run paid inference or test these models on a PAS7 repository.

The downloadable CSV [21] and JSON [22] preserve sources, effort settings, fallback notes, uncertainty where published and retrieval date. They include additional specialist results that would overwhelm the article.

Artificial Analysis uses max effort, with default fallback for Claude. Vals also includes server-side fallback and normally uses max, subject to benchmark overrides. Epoch ECI combines multiple configurations. An index point is specific to its publisher; these rows cannot be averaged. [4] [5] [6] [7] [13] [14] [15] [16] [17] [18]

The Epoch snapshot puts Opus at 168.83 and Astra at 168.81, with confidence intervals of 165.25–173.95 and 165.45–174.51. Sol and Sonnet also overlap these ranges. This does not establish a meaningful separation among all four. Vals puts Sonnet slightly above Opus, but their published uncertainty overlaps too. Artificial Analysis gives Claude a clearer lead on its own mix of tasks. Each index is answering a different weighted question. [13] [14] [15] [16] [17] [18]

A model label alone is insufficient to reproduce a result. Record the effort, tool budget, agent harness, context limits and whether a refusal caused a fallback to an older model. In particular, the AA Claude entries explicitly say “Max, Default Fallback”. These are results for the deployed route. They do not isolate every answer from the named model. [6] [7] [8]

Percent scores; higher is better within each row. AA and Vals runs of the same benchmark stay separate because their evaluation setups differ. AA SciCode is currently marked under review. Vals includes fallback-assisted tasks. [4] [5] [6] [7] [13] [14] [15] [16]

Sonnet leads this AA Terminal-Bench row and Vals’s Vibe Code and Code Migration rows. Opus opens a much larger gap on ProgramBench, which evaluates a different kind of complete programming task. Astra leads both displayed science-terminal runs. These differences support task-specific shortlists: UI iteration, long programming work and scientific tools are distinct workloads.

For a vibecoding project, start by making a failing test or a concrete acceptance brief. Ask each shortlisted model to finish the same bounded change with the same tools. Count accepted changes, regressions, human corrections, elapsed time and total spend. A beautiful preview can hide broken keyboard navigation or a failed build; a terminal benchmark does not measure your product’s visual quality.

We have not inserted a SWE-bench Verified score from an unrelated agent setup to fill a missing cell. Verified’s 500-instance scope is different from SWE-bench Multimodal, which appears in the downloadable Vals data. Neither name is interchangeable with Terminal-Bench. [20]

AA rows use the configurations above; FrontierMath is Epoch’s internal max-effort mean score. ARC uses the exact max configuration with the standard route; NR means no matching published observation in this snapshot. Percent scores except Omniscience, which is an index. [4] [5] [6] [7] [17] [19]

GPT-6.1 Sol reaches 100% on this FrontierMath tier-4 run, while Astra reaches 97.6%. That is evidence about this test, rather than proof that either can solve arbitrary mathematics. CritPt stays near 31% for all four in AA’s snapshot. Different scientific tasks still expose substantial gaps. Use tools, inspect derivations and validate the result independently.

ARC-AGI-3 also demonstrates how much the surrounding agent matters: Astra max scores 62.71% on the standard route and 98.55% with the provider adapter; Sol max moves from 52.73% to 96.18%. The adapter preserves and manages state differently. Comparing the adapter’s headline against another model’s standard run would confound the model with the harness. The table deliberately keeps those configurations separate. [19]

For long documents, capacity and retrieval quality are separate questions. AA’s Long Context Reasoning result favors Opus slightly; multilingual long-context results are much less uniform. These aggregate scores do not establish a separate winner for Ukrainian or German. For actual writing, compare terminology, factual grounding and revisions in the language you publish.

Rating points with the publisher’s uncertainty. Text and WebDev boards: October 22; vision: October 19; image-to-WebDev retrieved October 24. Effort: GPT max throughout; Opus high for text/vision and max for web; Sonnet xhigh throughout. Close scores with overlapping intervals are not decisive wins. [9] [10] [11] [12]

Opus leads these web-preference rows. In text, GPT-6.1 Sol, Sonnet and Astra are tightly clustered, while the vision intervals for Sol, Opus and Astra overlap. Preference voting is useful for perceived usefulness and presentation, but it does not guarantee factual accuracy, safe tool use or successful completion in a private codebase.

For research, keep the source documents and check every important citation. For agents, constrain permissions, review shell commands and run tests before accepting a change. Giving one model another model’s answer to critique can surface mistakes, but agreement between them is not independent verification. They can repeat the same unsupported premise.

AA Briefcase and GDPval use Elo; the other rows are percentages. Claude leads the two work ratings and AutomationBench here, while GPT leads GDPpdf all-pass. A broad document-work recommendation still depends on the document format and acceptance criteria. Configurations remain max, with default fallback for Claude. [4] [5] [6] [7]

Standard API USD per million tokens, plus a controlled example with 100,000 uncached input tokens and 10,000 total billable output tokens per call. No cache writes, premium speed, batch discount, tool fees or tax. Actual reasoning and tool loops change the token count. [1] [2] [3]

On AA’s weighted workload, Sonnet costs about $5.46 per task, Opus $5.98, Astra $3.26 and Sol $0.72. Sonnet and Sol have identical standard input/output rates, yet their measured workload costs differ substantially. Reasoning volume, repeated context and cache behavior all affect the result. This is AA’s workload, not a forecast for every project. [4] [5] [6] [7]

Sonnet produces roughly 133 output tokens per second in AA’s measurement, ahead of Opus at 96 and the GPT models near 57–58. That measures output throughput, rather than time to a finished patch. A model may think for longer before emitting an answer or require more repair turns.

All four accept text and images and produce text. Astra and Sol offer a 1,050,000-token context window with 128,000 maximum output; Claude offers 1M context and 128K output. A large window is not a promise to use every supplied token correctly. For OpenAI requests above 272K input tokens, the documented long-context multiplier is 2× input/cache and 1.5× output; our 100K example stays below that threshold. GPT-6.1 Sol tool calling uses the Responses API. Model API prices and a ChatGPT, Codex or Claude subscription are different purchasing units. [1] [2] [3]

A useful two-model workflow has an explicit escalation trigger: a failing test, an unresolved assumption, a risky migration or a change that touches authentication. Let Sol or Sonnet attempt routine work, run deterministic checks, and ask Opus or Astra to inspect a specific failure or disputed design. Give the reviewer the original requirement and evidence, not just the first model’s conclusion.

Under the controlled token example, one Sol call costs $0.30. Adding one Opus review to 20% of tasks makes the average $0.30 + 0.20 × $0.60 = $0.42. Calling both on every task costs $0.90; an Opus-only call is $0.60. This is our arithmetic under fixed token volumes, not a measured savings claim. Real review inputs, reasoning, retries and escalation frequency will differ.

Keep the same tools and budget when comparing routes. Record accepted results per dollar and per hour. Retain the pair only if it fixes enough errors to justify its extra latency and complexity; a second subscription or API provider also adds credentials, data handling and operational work.

GPT-6.1 Sol: start here for frequent, bounded code changes, batchable work and budget-sensitive agents. Its low cost in AA’s workload and strong FrontierMath result make it more than a disposable draft model. Verify UI quality and difficult multi-step changes before making it the only route.

Claude Sonnet 5.5: shortlist it for rapid interface iteration and migrations. The Vals Vibe Code and Code Migration results and AA output speed support that choice. Watch actual token consumption: a low list price can still produce an expensive max-effort session.

Claude Opus 5.5: shortlist it for longer autonomous programming tasks, document workflows and a second look at difficult changes. ProgramBench, work indexes and WebDev preferences support that role. Its higher cost and fallback-dependent published results are reasons to measure the complete route on your tasks.

GPT-6 Astra: consider it for scientific tools, demanding reasoning and reviewing assumptions that a Claude-based workflow keeps missing. Vals Science, science-terminal and ARC results support that shortlist. Its standard API rates are five times Sol’s, so require a visible improvement before routing routine work to it.

Our starting policy would be Sol or Sonnet for execution, deterministic checks, then a selective Opus or Astra review when the evidence calls for one. Keep a small, stable task set from your own work and rerun it after model or agent updates. Choose the route that delivers accepted results within your budget; a leaderboard position is a reason to test a model, not a substitute for that decision.