GPT-6 Astra vs Claude Opus 5 vs Grok 4.6: Complete Comparison and Report on Pricing, Benchmarks, Context Window, and Safety Disclosure

Three labs shipped their current top-tier public model within a seven-week window in mid-2026: Anthropic's Claude Opus 5 on July 24, xAI's Grok 4.6 on August 12, and OpenAI's GPT-6 Astra on September 3. Unlike a flagship-versus-mid-tier comparison, these three genuinely compete for the same workload and the same budget line, at three different price points: $5/$25, $2/$6, and $10/$50 per million tokens.

This report covers where the published numbers agree, where independent testing contradicts the vendor's own claims, and where one lab's disclosure practices differ sharply from the other two on the same question.

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RELEASE TIMELINE AND MODEL IDENTITY.

Model identifiers, lineage, and corporate context for each release.

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Attribute	GPT-6 Astra	Claude Opus 5	Grok 4.6
Vendor	OpenAI	Anthropic	xAI (now under SpaceXAI branding)
Release date	September 3, 2026	July 24, 2026	August 12, 2026
Model ID	gpt-6-astra	claude-opus-5	grok-4.6
Predecessor	GPT-5.6 Sol	Claude Opus 4.8	Grok 4.5
Days since predecessor	About 10 weeks	About 8 weeks	35 days
Position in vendor lineup	Sole flagship	Between Sonnet 5 and Fable 5	Sole flagship
Base model	New generation	Successor architecture	Same base as 4.5, post-training refinement

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The three releases sit at different points in their own lineups. Astra has no tier above it at OpenAI. Opus 5 is deliberately positioned as the practical middle option, between Sonnet 5's volume pricing and Fable 5's frontier ceiling, and Anthropic's own materials describe it as reaching near-Fable-5 intelligence at half the price. Grok 4.6 is xAI's flagship by default, since the company runs a single public model line rather than a tiered one, but xAI states plainly that it built 4.6 on the same roughly 1.5-trillion-parameter base as 4.5 rather than training a larger model, putting the entire capability gain into post-training.

The corporate context around Grok also changed during this period. Coverage from multiple outlets describes xAI merging into SpaceX in an all-stock deal that closed on February 2, 2026, with the combined company rebranding to SpaceXAI shortly after. xAI's own product documentation and API still refer to the model as Grok under the xAI name in most technical contexts, so this report uses xAI for technical attribution and notes the SpaceXAI branding where it appears in launch materials.

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PRICING AND RATE STRUCTURE.

Full API rates, including the long-context penalties each vendor applies differently.

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Rate per 1M tokens	GPT-6 Astra	Claude Opus 5	Grok 4.6
Standard input	$10	$5	$2 (under 200,000 input tokens)
Standard output	$50	$25	$6 (under 200,000 input tokens)
Cached input	$1.00	$0.50	$0.50
Fast/high-speed mode	2x price, up to 2.5x speed	2x price ($10/$50), about 2.5x speed	Not published as a distinct tier
Long-context penalty	Above 272,000 input tokens: 2x input/cache, 1.5x output, entire request	None	Above 200,000 input tokens: doubles to $4/$12 for entire request

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Astra is the most expensive of the three on every line, at twice Opus 5's rate and five times Grok 4.6's. Opus 5's pricing is unchanged from its predecessor Opus 4.8, a rare case of a capability jump landing at the same price point rather than a new one.

Grok 4.6 and Astra both apply a threshold that reprices the entire request rather than only the tokens above the line, but the thresholds sit at different points: 200,000 tokens for Grok, 272,000 for Astra. A workload that regularly sends prompts between those two figures crosses Grok's cliff while staying under Astra's, which changes which model actually costs less at that specific prompt length despite Grok's much lower headline rate.

Opus 5 has no equivalent long-context penalty, billing its full 1M-token window at the flat standard rate.

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CONTEXT WINDOW AND OUTPUT LIMITS.

Window sizes and the practical ceiling on long agent runs.

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Limit	GPT-6 Astra	Claude Opus 5	Grok 4.6
Context window	1,050,000 tokens	1,000,000 tokens	500,000 tokens
Maximum output	128,000 tokens	128,000 tokens	Not separately published
Knowledge cutoff	April 30, 2026	May 2026	February 1, 2026
Reasoning effort levels	low, medium, high, xhigh, max	low, medium, high	low, medium, high, xhigh

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Grok 4.6's context window is exactly half of Astra's and Opus 5's, and it did not change from Grok 4.5, since the September release cycle put its engineering effort into post-training rather than window expansion. For workloads built around large document sets or extended agent transcripts, that halves the effective ceiling before any pricing consideration.

Astra is the only one of the three offering an xhigh and max effort pair above high, while Opus 5 stops at three levels and Grok 4.6 sits in between with four, having added xhigh in this release after Grok 4.5 had no equivalent setting.

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BENCHMARK METHODOLOGY AND DISCLOSURE GAPS.

What each vendor published, and where sources actively disagree about what exists.

The three releases sit at three different points on architecture disclosure. Anthropic's Opus 5 launch materials describe benchmark methodology and effort settings in detail but, per public indexing checked after launch, no dedicated Opus 5 system card was visible separately from Anthropic's general safety documentation. OpenAI published a full system card for Astra with a Preparedness Framework rating. xAI's disclosure is the most contested: some launch-day coverage states xAI published a same-day model card, while other technical breakdowns published in the same week state directly that xAI released no system card and disclosed no parameter count, no mixture-of-experts structure, and no training compute for Grok 4.6. Both claims cannot be fully reconciled from public sources, and a reader relying on either single source would draw the wrong conclusion about what xAI actually disclosed.

A second, more concrete dispute concerns Grok 4.6's real-world benchmark trajectory. xAI's own launch table reports meaningful gains over Grok 4.5, including a jump on DeepSWE from the mid-50s to 65.9%. An independent early listing from LiveBench, however, reports the opposite direction on two separate measures: agentic coding down to 54.2 from Grok 4.5's 56.5, and SkillsBench down to 55.77 from 66.03, alongside a time-to-first-token increase from 8.7 to 31.2 seconds. A launch-week model showing a same-week independent regression on two benchmarks it did not previously trail on is a materially different situation than a straightforward capability upgrade, and it has not been resolved by further testing at the time of this report.

Terminal-Bench comparisons across all three models carry a version trap. xAI reports Grok 4.6 at 26% on Terminal-Bench v3.0, a substantially harder revision, while Artificial Analysis separately reports 88.39% for the same model on v2.1. Astra's published Terminal-Bench figure is 57.7% on v4.0. None of these three numbers describes the same test, and none should be placed in a single comparison row without the version attached.

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INTELLIGENCE INDEX AND COMPOSITE SCORES.

The one benchmark all three vendors' models have been placed on by the same independent evaluator.

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Model	Artificial Analysis Intelligence Index	Source
GPT-6 Astra	61.2	OpenAI launch table
Claude Opus 5	63.1	Artificial Analysis
Grok 4.6	61	Artificial Analysis

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On the single index where all three appear under one methodology, the spread is narrow: about two points separate the highest score from the lowest. Grok 4.6 ties GPT-5.6 Sol's score and sits one point behind Claude Fable 5, two behind Opus 5, and effectively level with Astra, according to Artificial Analysis's own framing of the result.

A composite index compresses a great deal of variance into one number, and the sections that follow show where the three models diverge sharply on specific task types despite this near-tie on the aggregate.

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AGENTIC CODING AND LONG-HORIZON BENCHMARKS.

Software engineering and multi-step task performance, where the version caveats above apply directly.

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Benchmark	GPT-6 Astra	Claude Opus 5	Grok 4.6
DeepSWE (long-horizon engineering)	74.1% (v1.1)	Not published in this table	65.9% (v1.1, xAI-reported; independent listing shows regression)
SWE-bench Verified	Not published for this comparison	96.0%	Not published
Frontier-Bench (agentic coding)	Not published	43.3% (vs 18.9% for Opus 4.8)	Not published
ARC-AGI-3	99.9% (stateful harness; independent stateless runs: 17-63%)	30.2% (about 3x the next-best model at launch)	Not published
OSWorld 2.0	72.6% partial	Surpasses Fable 5's best result at about a third of the cost	Not published

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Direct three-way comparison is not possible on most of these rows, since each vendor publishes a different subset of benchmarks and rarely tests its own model against both competitors under identical conditions. Astra's 74.1% on DeepSWE is the highest published figure among the three, ahead of Grok 4.6's contested 65.9%, with no equivalent Opus 5 figure available in public launch materials.

Astra's ARC-AGI-3 figure carries the harness caveat documented in this outlet's earlier coverage of the model: the 99.9% score used a stateful evaluation setup, while independent stateless runs on the same weights produced results between 17% and 63% depending on reasoning tier. Opus 5's 30.2% is not directly comparable to either Astra figure without knowing which harness produced it.

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REASONING, KNOWLEDGE, AND FINANCE/LEGAL AGENT BENCHMARKS.

Academic and domain-specific evaluation results.

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Benchmark	GPT-6 Astra	Claude Opus 5	Grok 4.6
GPQA Diamond	96.0%	Not published for this comparison	Not published for this comparison
FrontierMath Tier 4 v2	97.6%	Not published for this comparison	Not published for this comparison
Humanity's Last Exam, with tools	57.2%	Not published for this comparison	Not published for this comparison
Legal reasoning	Not a stated strength	Anthropic charts show Opus 5 trailing Fable 5 on legal and health evaluations	xAI states 4.6 wins knowledge work and legal on its own ten-row table

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The gaps in this table are as informative as the filled cells. None of the three vendors published a matching set of academic reasoning benchmarks for direct comparison, which means claims about which model is strongest at graduate-level reasoning cannot currently be settled from public data across all three; they can only be evaluated pairwise against whichever vendor chose to test that specific benchmark.

Anthropic's own disclosure that Opus 5 trails Fable 5 on legal and health evaluations is a rare instance of a vendor naming a weakness inside its own launch material rather than only inside a comparison built by someone else.

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UNIQUE DIFFERENTIATING FEATURES.

Capabilities specific to one model that do not appear as a benchmark row.

Grok 4.6's most defensible differentiator is not a benchmark score but a tool: native access to the X platform's live firehose through a first-party x_search function, priced separately at $5 per 1,000 calls. No competing model has licensed equivalent access to real-time social data at the API level, which makes it the practical choice for social listening or breaking-news monitoring regardless of how its coding benchmarks compare.

Astra's differentiator is its computer-use and asynchronous tool-calling stack, including a hosted shell, apply-patch tool, and the ability to continue reasoning on independent subtasks while a tool call is outstanding, aimed at long, semi-supervised agent sessions rather than short answers.

Opus 5's differentiator is the effort-and-speed trade built into a single model: a low/medium/high effort toggle for controlling reasoning depth per request, plus a separate Fast mode at double price for roughly 2.5x the generation speed, giving a team four practical cost-and-latency combinations from one model rather than a separate model per tier.

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AVAILABILITY AND REGIONAL RESTRICTIONS.

Where each model can actually be deployed today.

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Channel	GPT-6 Astra	Claude Opus 5	Grok 4.6
First-party API	Staged rollout	Available at launch	Available at launch
Amazon Bedrock	Yes	Yes	Yes, limited to us-east-1 and us-west-2
Google Cloud	Not listed	Vertex AI	Not listed
Microsoft	Azure	Microsoft Foundry	Not listed
Coding-agent platforms	Codex	Claude Code, Claude Cowork	Grok Build, Cursor
Consumer app	ChatGPT (staged)	Claude.ai, default on Claude Max	Grok Bot, grok.com

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Grok 4.6's Bedrock availability is the narrowest of the three, restricted to two AWS regions, which matters directly for any organization with data-residency requirements outside the United States. Opus 5 has the broadest enterprise cloud footprint of the three, available on all three major clouds at launch, while Astra's rollout was staged rather than immediate across its channels.

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WORKLOAD ALLOCATION CRITERIA.

Which of the three actually fits a given job, given the price spread and the disclosure gaps above.

The roughly two-point spread on the Intelligence Index means the composite score should not decide this choice; the price spread and the specific benchmark a workload depends on should. At $2/$6 under 200,000 tokens, Grok 4.6 is the cost leader by a wide margin and the only one of the three with native real-time social data access, making it the default candidate for high-volume agent work and any task that depends on current public sentiment, provided the 500,000-token window and the unresolved LiveBench regression are tested against the specific workload first rather than assumed away.

Claude Opus 5 sits at the price-to-capability midpoint, with the broadest cloud availability and a built-in effort dial that lets a single integration serve both cheap high-volume calls and slower high-effort ones, which makes it the reasonable default for teams that do not want to run three separate model integrations for three different cost tiers.

GPT-6 Astra costs the most per token of the three and should be reserved for work its price actually justifies: tasks near the edge of what any current model can do, long-context jobs safely under its 272,000-token pricing threshold, or computer-use and asynchronous agent work where its specific tool stack has no equivalent in the other two. Given that xAI's own DeepSWE gain for Grok 4.6 has an independent source reporting the opposite result, and that Anthropic has not published a dedicated safety card for Opus 5 at the same depth as OpenAI's Astra system card, any procurement decision across these three should treat vendor-reported benchmarks as a starting hypothesis to verify on real workload traffic, not as a settled ranking.

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