Claude Opus 5.5 vs GPT-6 Astra vs Grok 4.7: Which Should You Pick?

Three frontier models shipped within three weeks of each other. OpenAI released GPT-6 Astra on 3 September 2026. SpaceXAI (xAI's new name) shipped Grok 4.7 on 21 September, and Anthropic followed with Claude Opus 5.5 the next day. All three are aimed at the same buyer: teams running coding agents, long-document work and multi-hour agent tasks. They differ a lot on price. Astra's output tokens cost 8x Grok's, and Opus sits in between.

This comparison covers list prices, cache and long-context pricing, context windows, benchmarks on test suites all three vendors actually share, what Artificial Analysis measured independently, and where each model is available. We only compare numbers from the same benchmark version. Where a vendor hasn't published a score, the table says so. We don't estimate one.

Which model should you pick?

Here is the verdict first, with the evidence below it.

If you want one model and don't want to think about it, pick Opus 5.5 at its default medium effort. If you're choosing a second model for routing, pair it with Grok 4.7 for cheap bulk work, or with Astra for science-heavy tasks and runs where token count matters most.

How much do Opus 5.5, GPT-6 Astra and Grok 4.7 cost?

All prices below are per million tokens, taken from each vendor's own pricing page as of 22 September 2026.

Sources: Anthropic pricing, OpenAI GPT-6 Astra model page, xAI Grok 4.7 model page, xAI launch post (Fast variant).

The long-context rules matter more than the headline rates. Both OpenAI and xAI reprice the entire request once a prompt crosses their threshold. It's a cliff, not a tier where only the extra tokens cost more. Anthropic has no threshold at all. Here is what four request shapes cost at list price with no caching:

At a 400K-token prompt, Astra costs almost 5x what Opus 5.5 does, and Grok's price advantage over Opus has mostly gone. That's why Opus 5.5 wins the long-context row in the decision table, even though Grok is cheaper per token in normal use. For more on Astra's pricing cliff, see our GPT-6 Astra pricing breakdown.

Caching changes the picture again. Opus 5.5's cache hits cost $0.20 per million, which is 5% of its input price. Anthropic set a special 0.05x multiplier for this model. So a coding agent that re-reads a stable system prompt and repo context on every turn pays less for cached context on Opus 5.5 than on Grok 4.7 ($0.50) or Astra ($1.00).

What are the context windows and specs?

Two of these specs affect your bill. Neither Opus 5.5 nor Astra lets you turn reasoning off, so every call spends thinking tokens billed at the output rate. For Opus 5.5, the lever is low or medium effort, and its default is already medium. Grok 4.7 defaults to high. So if you call it without setting effort, you pay for more reasoning than you may need.

How do they compare on benchmarks?

Benchmark comparisons across these three are easy to get wrong. The vendors each chose different test suites, and several suites have been re-versioned in 2026. Terminal-Bench is now 4.0 and OSWorld is 2.0. The table below keeps only benchmarks where at least two of the three models have a published score on the same version. Where a number came from a competitor's announcement rather than the model's own vendor, we say so.

What the table says:

• Agentic coding goes to Opus 5.5. It leads Terminal-Bench 4.0 by 8.5 points over Astra and by about 28 over Grok, and it posts the highest CursorBench 4.0 score. On DeepSWE v1.1, Opus and Astra are effectively tied, with Grok 3 points behind.
• Astra wins on scientific workloads and business automation, but narrowly. Its 1.4-point lead on AutomationBench is inside the margin of noise. Its 5.9-point lead on Terminal-Bench-Science is not.
• Grok 4.7 is a clear tier below the other two on every shared benchmark. It only beats Astra on the two Elo-based knowledge-work measures (AA-Briefcase and GDPval), and both of those rows combine figures from different vendors' reports.
• Effort settings aren't matched. Anthropic ran Opus 5.5 at max effort on most tests, and at xhigh on Terminal-Bench. OpenAI reported Astra's Terminal-Bench at high effort. Close gaps could reverse under matched settings.

On computer use, don't compare the OSWorld headline numbers directly. Opus 5.5 scores 81.8% on OSWorld 2.0 (partial credit; 48.7% strict). OpenAI reported Astra at 72.6% on an offline subset of OSWorld 2.0. Those are different measurements. xAI hasn't published an OSWorld score for Grok 4.7.

What does Artificial Analysis say?

Artificial Analysis (AA) runs the same ten-eval suite on every model with the same harness. That makes it the best available head-to-head for these three. The numbers below come from each model's AA page on Intelligence Index v4.3.2.

This is where the rate cards stop telling the whole story. Grok 4.7's list price is one-eighth of Astra's on output, yet the whole AA suite cost almost the same on both, because Grok produced four times the output tokens. Opus 5.5 at max effort is the most expensive to run end to end, at about 119k output tokens per task on AA's measurement. It also scores 5 points higher than anything else. If you only count list price, Astra looks like the premium option. Once you count tokens per task, it's the most efficient of the three.

Two caveats. First, these are max- and xhigh-effort numbers. Anthropic's own CursorBench data shows Opus 5.5 at medium effort scoring 52.5% at about $3 per task, against 57.8% at max. Most production workloads should run there, and the token bill falls sharply. Second, AA rebased the index between versions. Older published figures, including the 61 we cited for Astra at its launch, aren't comparable with v4.3.2 scores. Only compare scores from the same index version.

On speed, the picture is incomplete. AA hadn't measured Opus 5.5's throughput when we checked. Anthropic says it's more than 30% faster than Opus 5, and Fast mode offers up to 2.5x at double the price. AA calls Grok 4.7 "notably slow" at 39.2 tok/s and "very verbose". Astra's time to first answer token is long, because it reasons before replying.

Which is best for coding agents?

Opus 5.5, on current evidence. It leads both terminal-agent benchmarks that all three vendors report, and it ties Astra on DeepSWE. Anthropic's launch customers describe token savings at the same quality: Kiro reported about 40% fewer calls, and Optiver said it matched Opus 5 "in about half the turns." We cover the harness side in our AI coding agents guide.

Grok 4.7 is the value pick for coding. On xAI's own numbers it gained 17.7 points on Terminal-Bench 4.0 over Grok 4.6, and AA's Coding Agent Index puts Grok 4.7 with Grok Build at 56. That's 4th among native harnesses, behind Fable 5.1, Astra and Opus 5. Developers in the Hacker News launch thread report using it as an extra code reviewer that catches issues other models miss. Details are in our Grok 4.7 guide and the Grok 4.7 vs 4.6 comparison.

Astra suits long autonomous runs where token count matters more than per-token price, and scientific and data-heavy repos. It also comes with operational conditions. OpenAI runs misalignment monitoring on all tool-using Astra inference, and that monitoring can pause a conversation for review. Plan for that in unattended pipelines.

Which is best for long-context work?

Opus 5.5, clearly. It has a 1M-token window at flat pricing, $0.20 cache reads, and a long-context result in Anthropic's system card (ProgramBench 91.2%, against 85.4% for Opus 5). Astra's window is slightly larger on paper at 1.05M, but its input caps at 922K, and every request over 272K costs about twice as much. Grok 4.7 stops at 500K and moves to its higher tier from 200K. If your prompts regularly exceed 200K, run the cost table above against your real traffic before choosing on list price.

Which is cheapest for high-volume workloads?

Grok 4.7 per token, with conditions. Keep prompts under 200K, set effort to medium or low rather than the high default, and cap output length. It generated roughly 2.25x the tokens of Grok 4.6 on AA's suite, so its cost per task rises faster than its rate card suggests. For batch jobs, Opus 5.5 at $2/$10 is competitive. xAI hasn't documented a batch discount for Grok 4.7.

If cost matters most, none of these three is the right tier. OpenAI's GPT-6 Sol ($2/$10) and Luna ($0.10/$0.50) launched the same day as Opus 5.5. See our GPT-6 Sol and Luna guide, the Opus 5.5 vs GPT-6 Sol comparison, and our cheapest fast LLM APIs roundup.

Where can you use each model?

Sources: GitHub changelog (Opus 5.5), GitHub changelog (Astra), GitHub changelog (Grok 4.7), Anthropic announcement.

What are the gotchas before you switch?

• Opus 5.5 breaks some API calls. thinking: {"type":"disabled"} and forced tool_choice both return 400 errors. Thinking blocks are tied to the conversation, so edit-and-replay patterns fail on newer accounts. Our Opus 5.5 migration guide covers the fixes.
• Opus 5.5 safeguards reroute some requests. Anthropic says most cybersecurity tasks go to Opus 4.8, and 2.5–3.9% of benchmark requests hit a fallback model. Security teams should test before switching.
• Astra has a pricing cliff and monitoring. Add an alert for prompts over 272K. Also expect Astra's safety monitoring to occasionally pause long tool-using runs.
• Grok 4.7 is verbose and returns encrypted reasoning. The Responses API returns reasoning content encrypted, so you can't inspect it. Budget for more output tokens per task than Grok 4.6 used.

# Same prompt, three providers. Set effort explicitly on each.
# Parameter names follow each vendor's docs at launch; check the xAI docs
# for the current reasoning-effort field before relying on it.
import anthropic, openai

PROMPT = "Refactor utils/date.py to remove the pytz dependency."

claude = anthropic.Anthropic()
r1 = claude.messages.create(
    model="claude-opus-5-5", max_tokens=16000,
    output_config={"effort": "medium"},
    messages=[{"role": "user", "content": PROMPT}],
)

oai = openai.OpenAI()
r2 = oai.responses.create(model="gpt-6-astra", reasoning={"effort": "medium"}, input=PROMPT)

xai = openai.OpenAI(base_url="https://api.x.ai/v1", api_key=XAI_KEY)
r3 = xai.chat.completions.create(model="grok-4.7", reasoning_effort="medium",
                                 messages=[{"role": "user", "content": PROMPT}])

Run your own evaluation on 20–50 real tasks at matched effort, and log both the score and the tokens used. The published tables above can't tell you which model is cheapest on your workload. Tokens per task can.

FAQ

Is Claude Opus 5.5 better than GPT-6 Astra?

On most shared benchmarks, yes. Opus 5.5 leads on Terminal-Bench 4.0 (66.4% vs 57.9%), HLE with tools, GDPval-AA and the Artificial Analysis Intelligence Index (58 vs 53), and it costs 60% less per token. Astra wins on Terminal-Bench-Science and AutomationBench, and it uses about a quarter of the output tokens.

Is Grok 4.7 better than Claude Opus 5.5?

No. Opus 5.5 leads Grok 4.7 on every benchmark both vendors publish, including Terminal-Bench 4.0 (66.4% vs 37.6%) and CursorBench 4.0 (57.8% vs 46.3%), and it scores 12 points higher on the AA index. Grok 4.7's advantage is price: $2/$6 against $4/$20 per million tokens.

Grok 4.7 vs GPT-6 Astra: which is cheaper in practice?

Grok 4.7 is 5x cheaper on input and about 8x cheaper on output, but it is very verbose. Artificial Analysis spent $4,967 running its full index on Grok 4.7 against $5,324 on Astra, which is nearly the same. For short prompts with capped output, Grok is much cheaper. For long reasoning runs, the gap narrows.

What is the best AI model in September 2026?

By Artificial Analysis's independent index, Claude Opus 5.5 is ranked #1 of 212 models with a score of 58, ahead of GPT-6 Astra and Claude Fable 5.1 (both 53). For coding agents and long-context work it's our default pick. "Best" still depends on the workload: Astra leads on science tasks, and Grok 4.7 on price.

Which has the largest context window?

GPT-6 Astra lists 1,050,000 tokens, but its maximum input is 922,000. Claude Opus 5.5 offers 1,000,000 at flat pricing, and Grok 4.7 offers 500,000. For prompts above 272K, Opus 5.5 is far cheaper than Astra.

Are GPQA or SWE-bench Verified scores available for all three?

No. OpenAI reports 96% on GPQA Diamond for Astra, but Anthropic and xAI haven't published GPQA or SWE-bench Verified scores for Opus 5.5 or Grok 4.7. All three vendors now report newer suites such as Terminal-Bench 4.0, DeepSWE v1.1 and CursorBench 4.0.

For the individual deep dives, see our Claude Opus 5.5 guide and GPT-6 Astra guide. If your team is building agents or products on these models and needs more engineering capacity, Codersera can help you hire vetted remote developers who already work with them.

Sources

• Anthropic: Claude Opus 5.5 announcement and system card
• Anthropic API pricing
• OpenAI: GPT-6 Astra model reference and launch post
• xAI: Grok 4.7 launch and model docs
• Artificial Analysis model pages: Opus 5.5, GPT-6 Astra, Grok 4.7, and Benchmarking Grok 4.7