Claude Opus 5.5 vs GPT-6 Astra vs Gemini 4 Argon vs Grok 4.7: Benchmarks and Real Cost per Task (Oct 2026)

Updated 2026-10-26: Added Gemini 4 Argon, which became generally available on October 13, with per-task pricing before and after its December 1 price increase. Refreshed Artificial Analysis scores and cost per task for each effort level, and corrected several pricing and benchmark figures. Added Muse Wonder Full and Kimi K3.1 Pro as alternatives.

Four frontier models now compete for the same buyers: teams running coding agents, long-document pipelines and multi-hour agent tasks. OpenAI's API changelog lists GPT-6 Astra's release on 3 September. xAI, whose launch page now carries the SpaceXAI name, shipped Grok 4.7 on 21 September, and Anthropic released Claude Opus 5.5 the next day. Google announced Gemini 4 Argon on 30 September for its Fairwind Program cyber defenders, then made it generally available on the paid Gemini API on 13 October.

The four differ less on headline intelligence than on price structure. Argon's introductory price ends on 30 November. Astra reprices the whole request above 272K tokens. Grok does the same at 200K, and Opus 5.5's real cost depends heavily on the effort level you pick. This page compares list and cached prices, specs, vendor-reported benchmarks (with the vendor named on every row), AA's independent cost per task, and where each model is available. It ends with a verdict for each workload.

One rule throughout: we only compare scores from the same benchmark version. When two vendors report different numbers for the same model on the same test, we show both.

Which model should you pick?

Verdict first; the evidence follows.

If you want one default and no routing, use Opus 5.5 at high: it is close to Argon on AA today and keeps its price after December. If you route, the strongest two-model setup today is Opus 5.5 for terminal and shell-heavy agent loops, and Argon for long-horizon repo tasks and workflow automation. Re-check that pairing in December, when Argon's price doubles.

How much do Opus 5.5, Astra, Argon and Grok 4.7 cost?

The table shows prices per million tokens from each vendor's pricing page, as of October 2026.

Sources: Anthropic pricing, OpenAI pricing, Gemini API pricing, xAI Grok 4.7 model docs, xAI Grok 4.7 launch. For a deeper look at Astra's rate card, see our GPT-6 Astra pricing guide.

Three things on this rate card affect real bills more than the headline numbers:

• Argon's December 1 doubling covers every line. Input, output, cached input, Batch, Flex and Priority all double. If token usage stays the same, every Argon workload costs exactly twice as much from December 1. After that date, Argon's standard rates match Opus 5.5's: $4 / $20 / $0.20 cached.
• Astra and Grok have pricing cliffs. Once a prompt crosses 272K tokens (Astra) or 200K (Grok), the higher rate applies to every token in the request, not only the tokens above the threshold. Opus 5.5 and Argon have no threshold.
• Argon's caching is implicit only. Google applies the 95% discount automatically when a prefix matches recent traffic. You can't create and pin a cache the way you can on Anthropic's or OpenAI's APIs. Put stable content (system prompt, tool schemas, repo context) first, and expect less predictable hit rates.

What common request shapes cost

These are list prices with no caching. Each figure is input tokens × input rate plus output tokens × output rate.

Worked check for the 400K row: Opus 5.5 costs 0.4 × $4 + 0.01 × $20 = $1.60 + $0.20 = $1.80. Astra crosses 272K, so the whole request bills at long-context rates: 0.4 × $20 + 0.01 × $75 = $8.00 + $0.75 = $8.75. Grok is at or above 200K, so it bills 0.4 × $4 + 0.01 × $12 = $1.60 + $0.12 = $1.72.

Worked example: a cached agent turn

Assume a coding agent turn with 150K input tokens, of which 130K are cache hits, plus 8K output tokens. The prompt stays under every pricing cliff.

This example holds output fixed at 8K tokens. In practice, the models produce very different amounts of output for the same task, and that's where rate-card comparisons break down. Grok 4.7 in particular uses far more output tokens. The next section uses AA's measured cost per task, which captures that difference.

What does each model really cost per task?

Artificial Analysis runs the same evaluation suite on every model with the same harness, then reports the Intelligence Index score and the average cost per index task at list prices. Because it captures verbosity and reasoning length, it's the best public measure of what a task actually costs. AA's Gemini 4 Argon page labels the index as v4.3.2. The figures below come from the AA leaderboard as fetched on the evening of 26 October 2026. AA revised several cost figures that day: earlier on 26 October it listed Argon at $1.99 per task and Grok 4.7 at $3.74 (xhigh) and $2.73 (high). We use the later figures. Speed and latency are live measurements that move from day to day, so treat them as approximate. AA labels its Opus 5.5 runs "with fallback": a small share of requests go to a fallback model when Anthropic's safeguards trigger.

What this shows:

• The effort setting matters more than the vendor. Moving Opus 5.5 from max to high drops 4 index points (58 to 54) and cuts cost per task by about 70% ($5.98 to $1.82). Going from high to xhigh adds 2 points for $1.64 more per task. For most production workloads, high is the setting to beat.
• Until 30 November, Argon is the value leader on AA's measure: 53 points for $1.62 per task, against 54 for $1.82 for Opus 5.5 at high. That's about $0.031 per index point against $0.034. The December price change reverses this (see below).
• Grok 4.7 is cheap per token but not per task. At xhigh it costs $3.24 per task for a score of 46, about the same as Astra at max effort ($3.26 for 53). At high it costs $2.36 for the same 46, which is still more than Opus 5.5 at high ($1.82 for 54). The low rate card doesn't make up for how many tokens it generates.
• Astra's per-task cost is moderate despite its $50 output rate, because it uses relatively few output tokens.

Cost per task before and after Argon's December 1 price change

AA's Argon page shows base prices of $2 input and $10 output (with a 95% cache discount), so the $1.62 figure reflects introductory pricing. Every Argon rate doubles on December 1. If Argon's token usage stays the same, its cost per task doubles too: $1.62 × 2 = $3.24. AA hasn't published that figure. It's our projection.

Cost per index point is cost per task divided by score. For example, $1.82 ÷ 54 = $0.034 and $1.62 ÷ 53 = $0.031. After December 1, Argon at high is projected at about $3.24 per task. That's roughly what Astra costs at max for the same score of 53 ($3.26), close to Opus 5.5 at xhigh ($3.46, 3 points higher), and nearly twice Opus 5.5 at high ($1.82, 1 point higher). Argon's case then rests on the specific workloads where it leads (DeepSWE, AutomationBench, long video and very long context), not on general value. AA's index is a broad average and doesn't weight those workloads. If they make up most of your traffic, run your own evaluation before writing Argon off.

Speed: once generating, the four are close. Opus 5.5 is fastest only at max effort (about 98 tok/s). At the recommended high setting it runs about 80 tok/s, level with Argon (80) and below Grok 4.7 (85 at high, 88 at xhigh). Astra is the slowest generator at 63–68 tok/s. The bigger difference is latency to the first chunk, which includes thinking time. At high, AA measures about 34 seconds for Opus 5.5, 48 for Argon, 49 for Grok 4.7 and 103 for Astra. At max effort the wait grows to about 367 seconds for Astra and 706 for Opus 5.5. For interactive tools, drop the effort level: Opus 5.5 at medium starts in about 19 seconds.

How do they compare on benchmarks?

Vendors choose different suites and sometimes report different numbers for the same model on the same test. Most of the Argon comparison below comes from Google's launch table. Google's blog presents that table as an image, so we used the reproduction in HackerNoon's comparison. Google's post states the Argon figures for DeepSWE v1.1, AutomationBench, LVBench and CWE-bench v1 directly in its text. Treat every row as vendor-reported.

Google's head-to-head table (Google-reported)

The results split by task type. Argon leads on long-horizon repo work, multi-app automation, finance and legal agent tasks, and very long inputs. In Google's own reporting, it loses on terminal-driven agent loops (Terminal-Bench 4.0 is 9 points behind Opus 5.5) and on FrontierSWE v2 (10.5 points behind Astra). If your agents mostly run shell commands in tight loops, Google's table doesn't support switching to Argon.

Other vendor-reported results, including Grok 4.7

Two disagreements are worth noting. On AutomationBench, Anthropic reports 40.0% for Opus 5.5 and Google's table reports 42.5%. On Terminal-Bench Science 0.1, Google's numbers are 4.6 points higher than Anthropic's for Opus 5.5 (63.3% vs 58.7%) and 3.5 points higher for Astra (68.1% vs 64.6%). Neither changes who leads each row, but don't mix numbers from different tables in your own comparisons.

Grok 4.7 trails on the shared coding benchmarks (Terminal-Bench 4.0, DeepSWE v1.1, CursorBench 4.0). On xAI's GDPval chart it scores above Astra (1,695 vs 1,542), and on Harvey's Legal Agent Benchmark xAI reports 19.6%, level with Argon in Google's table and well above Opus 5.5 (3.8%) and Astra (5.4%). Those cross-vendor comparisons mix reporters, so treat them as indicative.

Computer use: Anthropic reports 81.8% (partial credit) for Opus 5.5 on its OSWorld run. Google's table lists Argon at 69.2% and Astra at 72.6% on an offline subset of OSWorld-2.0, also with partial credit. Those are different measurements, and xAI hasn't published an OSWorld score for Grok 4.7.

¹ Terminal-Bench 4.0 vs Terminal-Bench 4.0-AA. In this article, "Terminal-Bench 4.0" means the score each vendor reported for its own model (or cited for a rival) on its launch page. Meta's Muse Wonder model card uses a separately labeled Terminal-Bench 4.0-AA row, which, according to Meta's table note as reproduced by VentureBeat, combines third-party and Meta-run results. On that row Opus 5.5 scores 59.6, Astra 59.1 and Muse Wonder Full 57.1, well below Opus 5.5's vendor-reported 66.4%. The two are not interchangeable. Even within vendor-reported Terminal-Bench 4.0, Astra appears as 57.9% (OpenAI's figure, cited by Anthropic) and 58.2% (cited by Google and Moonshot).

Independent check: Vals Index

Google's table shows Argon leading the Vals Index (68.9% vs Opus 5.5's 67.0%). Vals AI's own leaderboard, last updated 24 October 2026, now has Opus 5.5 slightly ahead at 70.18%, with Argon at 69.24%, Muse Wonder Full at 66.79%, Astra at 65.02%, Kimi K3.1 Pro at 62.52% and Grok 4.7 at 58.56%. The gap between Opus and Argon is under a point either way, which is effectively a tie. The broader pattern matches AA: Opus 5.5 and Argon sit together at the top, Astra a few points behind, and Grok 4.7 a tier below.

What are the context windows and specs?

Sources: Anthropic models overview, OpenAI GPT-6 Astra model page and API changelog, Gemini 4 Argon model page, xAI Grok 4.7 model docs.

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. On Opus 5.5 the lever is a lower effort level, and its API default is already medium. Grok 4.7 defaults to high, so calling it without setting effort pays for more reasoning than many tasks need.

Argon's "1M output" headline has conditions. The window is shared between input and output, and a standard synchronous request stops at 65,536 output tokens unless you use long decode. Plan to chunk whole-repository rewrites, or design for Google's long-decode path from the start. Our Gemini 4 Argon guide covers the exact limits, thinking levels and Vertex AI regions.

Which is best for coding agents?

It depends on the shape of the loop. (For the harness side, which matters as much as the model, see our AI coding agents guide.)

• Shell-heavy agent loops (run tests, read errors, patch, repeat): Opus 5.5. It leads Terminal-Bench 4.0 in every vendor's table that includes it, including Google's. It also posts the highest CursorBench 4.0 score published (57.8%). Details are in our Claude Opus 5.5 guide.
• Long-horizon repository tasks (multi-file changes over hours): Of the four models compared here, Argon has the strongest vendor-reported DeepSWE v1.1 result (77.9%). Its very long-context results (GraphWalks 256K–1M) also suggest it holds up when the agent carries a lot of repo state. Until 30 November, it's also the cheapest per token of the four for prompts over 200K.
• Repos heavy in science and data: Astra leads Terminal-Bench Science 0.1 and FrontierSWE v2. It costs the most per token, but AA shows it is moderate per task because it generates fewer tokens. See our GPT-6 Astra guide.
• Budget second opinion: Grok 4.7 works as an extra reviewer, especially in Cursor. Cap output and set effort below xhigh: AA measures it at $2.36 per task at high, compared with $3.24 at xhigh, for the same score of 46. See our Grok 4.7 guide and the Grok 4.7 vs 4.6 comparison.

Which is best for long-context work?

Opus 5.5 and Argon both bill a 1M window at flat rates. Anthropic's pricing page says a 900K-token request costs the same per token as a 9K one, and Google lists no long-context tier for Argon. Until 30 November, Argon is half the price of Opus 5.5 on long prompts. From 1 December, the rate cards are identical, and the decision comes down to caching and quality. Opus 5.5 has explicit caching at $0.20, while Argon's caching is implicit only. On quality, Argon leads Google's GraphWalks 256K–1M row (84.2% vs 66.8%). Astra reprices the whole request above 272K, and Grok 4.7 tops out at 500K with a higher tier from 200K.

Where can you use each model?

Sources: Anthropic models overview, Google Cloud: Gemini 4 Argon, Google availability post, xAI launch post, GitHub changelog for Opus 5.5, GPT-6 Astra and Grok 4.7, Cursor: joining SpaceX.

What are the gotchas before you switch?

• Argon's price doubles on December 1. Budget on $4/$20, not $2/$10, for anything that runs into next year. Batch and Flex ($2/$10 from December 1) bring offline work back to roughly today's standard price.
• Argon needs a paid project. Google says it is not available on the Free tier, so CI or staging environments running on free keys will fail when you switch the model ID.
• Opus 5.5 rejects some older request patterns. Disabling thinking and forcing tool_choice return errors. Our Opus 5.5 migration guide covers the fixes.
• Opus 5.5 safeguards can reroute requests. AA's "with fallback" label reflects this. Security teams should test their real prompts before switching.
• Astra's 272K cliff, and misalignment monitoring. Add an alert for prompts that approach 272K tokens. One oversized request costs roughly 2x on input and 1.5x on output for the whole request (see our GPT-6 Astra pricing guide). OpenAI's GPT-6 guide also says its systems asynchronously monitor Astra requests for misalignment and trigger alerts when necessary, so test long unattended tool-using runs before relying on them.
• Grok 4.7 is verbose, has a 200K cliff and returns encrypted reasoning. Set effort explicitly (the default is high), cap output, and keep prompts under 200K. On the Responses API, xAI returns Grok 4.7's reasoning as encrypted content plus summaries, so you can't inspect the full reasoning.

# Same prompt, four providers, effort set explicitly on each.
# Parameter names follow each vendor's docs; re-check before production use.
import os
import anthropic, openai
from google import genai
from google.genai import types

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": "high"},
    messages=[{"role": "user", "content": PROMPT}],
)

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

gem = genai.Client()  # paid-tier key in GEMINI_API_KEY
r3 = gem.models.generate_content(
    model="gemini-4-argon", contents=PROMPT,
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="high")),
)

xai = openai.OpenAI(base_url="https://api.x.ai/v1", api_key=os.environ["XAI_API_KEY"])
r4 = xai.chat.completions.create(model="grok-4.7", reasoning_effort="high",
                                 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. Published tables can't tell you which model is cheapest on your workload. Measured tokens per task can.

If your team is building agents on these models and needs more engineering capacity to evaluate, route and ship them, Codersera can help you hire vetted remote AI developers.

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% as reported by OpenAI), HLE with tools and GDPval-AA, and on the AA Intelligence Index (58 vs 53 at max effort). Its per-token price is 60% lower: $4/$20 against $10/$50. Astra wins on Terminal-Bench Science 0.1 and FrontierSWE v2.

Is Gemini 4 Argon better than Claude Opus 5.5?

For some workloads. In Google's table, Argon leads on DeepSWE v1.1 (77.9% vs 74.2%), AutomationBench (51.3% vs 42.5%), and long-video and very-long-context tests. It trails on Terminal-Bench 4.0 (57.4% vs 66.4%) and FrontierSWE v2 (55.0% vs 62.3%). On AA, Opus 5.5 at high scores 54 for $1.82 per task, against Argon's 53 for $1.62 until 30 November (a projected ~$3.24 after Argon's price doubles). On the independent Vals Index (24 October), the two are within a point of each other.

Gemini 4 Argon vs GPT-6 Astra: which should I pick?

Until 30 November, Argon is the better value on AA. It scores 53 for $1.62 per task, which beats Astra at high (51 for $1.73) and matches Astra at max (53) for about half the cost ($3.26). Argon also leads on DeepSWE, AutomationBench and long context, and Astra leads on FrontierSWE v2 and Terminal-Bench Science. After December 1, Argon's projected ~$3.24 per task is about the same as Astra at max and nearly twice Astra at high.

Is Grok 4.7 better than Claude Opus 5.5?

Not for most work. Opus 5.5 leads on every benchmark both Anthropic and xAI publish, including Terminal-Bench 4.0 (66.4% vs 37.6%), CursorBench 4.0 (57.8% vs 46.3%) and GDPval (1,846 vs 1,695, though xAI doesn't state its version), and it scores 12 points higher on AA (58 vs 46). The exception is legal agent work: xAI reports 19.6% for Grok 4.7 on Harvey's Legal Agent Benchmark, while Google's table has Opus 5.5 at 3.8% (different reporters). Grok 4.7's main advantage is its $2/$6 rate card, and its verbosity erases most of that on AA's per-task measure ($2.36 at high vs $1.82 for Opus 5.5 at high).

What is the best frontier model in October 2026?

On AA's index, Claude Opus 5.5 at max effort leads with 58. Muse Wonder Full (56, subscription-only) and Kimi K3.1 Pro (55) come next among the models covered here, followed by GPT-6 Astra and Gemini 4 Argon (53). For coding agents and general use, Opus 5.5 at high is our default. Argon is the pick for long-horizon repo work and automation, Astra for science-heavy tasks, and Grok 4.7 for price-capped bulk work.

When does Gemini 4 Argon pricing change?

Google's pricing page lists $2 input / $10 output / $0.10 cached per million tokens through 30 November 2026, then $4 / $20 / $0.20 from 1 December 2026. Batch, Flex and Priority double on the same date.

Sources

• Google: Gemini 4 Argon announcement
• Google: Gemini 4 Argon availability (13 October)
• Gemini API pricing, models and changelog
• Gemini API: Gemini 4 Argon model page and Google Cloud: Gemini 4 Argon
• Anthropic: Claude Opus 5.5, models overview and API pricing
• OpenAI API pricing, GPT-6 Astra model reference, API changelog and Using GPT-6 guide
• xAI: Grok 4.7 model docs, reasoning guide and Grok 4.7 launch
• Cursor: joining SpaceX (14 August 2026)
• GitHub changelog: Opus 5.5, GPT-6 Astra, Grok 4.7
• Artificial Analysis leaderboard, Gemini 4 Argon page, Grok 4.7 page and Muse Wonder article
• Vals AI: Vals Index
• Meta: Muse Wonder and Moonshot: Kimi K3.1 Pro
• HackerNoon: reproduction of Google's Argon benchmark table and VentureBeat: Meta's Muse Wonder table