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Claude Opus 5.5 vs GPT-6: Why Sol Is the Real Rival.
Claude Opus 5.5 scores 58 on the Artificial Analysis index against GPT-6 Astra's 53 and Sol's 48. But Sol costs half as much and runs faster. Full breakdown.
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Gemini 4 Argon tops the Vals Index at 68.9% but trails Claude Opus 5.5 on terminal coding, and its $2/$10 price doubles on Dec 1. See who should switch now.
Google announced Gemini 4 Argon on September 30, 2026, then gave it only to vetted cyber defenders (TechCrunch, 2026). On October 13, it opened to paid API customers, AI Studio and the Gemini app. Today it reached Google Cloud.
Should your team move workloads to it? Google's own table shows Argon beating GPT-6 Astra and Claude Opus 5.5 on most knowledge work, and losing clearly on terminal coding. There's also a pricing catch with a date on it: December 1.
Here's what Argon is, what it really costs, how safe it is, and a straight call by workload. For the other two labs, see our Claude Opus 5.5 vs GPT-6 comparison.
Key Takeaways
- Argon ranks first on the independent Vals Index at 68.9% (Vals AI, October 2026).
- It trails Claude Opus 5.5 by 9 points on Terminal-Bench 4.0, per Google's own table.
- Pilot it now for long documents, legal and finance work. Its $2/$10 price doubles on December 1.
Google's model card, published in October 2026, describes Gemini 4 Argon as a new sparse mixture-of-experts model, "not a modification or a fine-tune of a prior model" (Google DeepMind, Gemini 4 Argon Model Card, 2026). It's aimed at long, multi-step work: code migrations, legal and finance research, and cyber defense.
| Spec | Gemini 4 Argon |
|---|---|
| Model ID | gemini-4-argon |
| Context window | 1M tokens, shared by input and output |
| Max output | 65,536 by default, up to 1M with long decode |
| Inputs / output | Text, image, video, audio, PDF in; text out |
| Thinking levels | Low, medium, high |
| Caching | Implicit only |
| Knowledge cutoff | March 2026 |
The 1M output ceiling is the standout, up from 64K on the previous model (Google, Gemini 4 Argon: our next era of frontier intelligence, 2026). Who needs a million tokens out? Teams doing big migrations. Google says its agents are moving C and C++ to Rust with Argon, up to 800K+ lines for the Fuchsia Zircon kernel.
Google's model card shows Argon leading on 13 of 19 benchmarks against the best of GPT-6 Astra, Claude Fable 5.1 and Claude Opus 5.5, as of October 2026 (Google DeepMind, Gemini 4 Argon Model Card, 2026). It ties one and trails on five. The wins are in knowledge work and long context. The losses are in coding loops.
The biggest gap is legal work. On Harvey's Legal Agent Benchmark, Argon scores 19.6% while the next best, Fable 5.1, scores 6.7%. On GraphWalks at 256K to 1M tokens, Argon hits 84.2% against 71.8% for GPT-6 Astra. If you read contract archives or long recordings, that's real money.
These are Google's numbers, though. So does anyone independent agree? Yes, in part. On the Vals Index, updated October 13, 2026, Argon ranks first at 68.90%, ahead of Claude Sonnet 5.5 at 67.04% and Opus 5.5 at 66.97% (Vals AI, Vals Index, 2026).
The losses are just as clear. Argon scores 57.4% on Terminal-Bench 4.0 against 66.4% for Opus 5.5, and trails GPT-6 Astra by 10.5 points on FrontierSWE v2. Artificial Analysis's own Terminal-Bench run narrows the gap, with Argon at 57.1% and Sonnet 5.5 leading at 63.6% (eesel, Gemini 4 Argon review, 2026).
It's still strong at big, careful rewrites. Google says Argon replaced 32K lines of SIMD code in the libgav1 decoder with memory-safe Rust that runs 2.7x faster than the existing Rust port.
Our take: Argon wins when the job is "read a lot, then write carefully." It loses when the job is "run a command, read the error, try again" hundreds of times. Sort your workloads that way before you test anything.
Gemini 4 Argon costs $2 per million input tokens and $10 per million output tokens through November 30, 2026. From December 1, that doubles to $4 and $20 (Google, Gemini API pricing, 2026). Batch and Flex run at half price, and cached input is 95% off. It isn't on the API free tier.
Price per token is only half the story, because Argon is wordy. It used 110M output tokens on the Artificial Analysis Intelligence Index, against a median of 85M (Artificial Analysis, Gemini 4 Argon, 2026). GPT-6 Astra used 60M (Artificial Analysis, GPT-6 Astra, 2026). Caching is implicit only, so put system prompts and reference files first to get the discount.
So what does a task really cost? Artificial Analysis scores Argon 53 on its Intelligence Index, level with GPT-6 Astra and Fable 5.1, and five points behind Opus 5.5 at 58 (Artificial Analysis, Intelligence Index leaderboard, 2026). Today a task costs $1.99. At December's prices, we estimate about $3.98.
Our take: Claude Opus 5.5 on its "high" setting scores 54, one point above Argon, for $1.82 per task. After December 1, that's less than half of Argon's cost. If you're buying general reasoning, Argon isn't the value pick. Its case rests on its legal, finance and long-context wins.
In its October 2026 model card, Google says it "cannot rule out" that Argon has reached its critical capability levels for biological risk and cyber offense (Google DeepMind, Gemini 4 Argon Model Card, 2026). Google says added safeguards bring both risks below those thresholds. The same card shows unjustified refusals down 9.6 points against Gemini 3.1 Pro.
For agents that read email and web pages, prompt injection matters most. On the Gray Swan benchmark, attacks against Argon succeeded 0.7% of the time across 15 adaptive attempts (Google, Gemini 4 Argon: Built to solve your hardest problems, 2026). That's the best result Google published, but it isn't zero. If 10,000 documents a day carried injection attempts, about 70 could still get through. Sandboxing stays your job, as our look at AI sandbox escapes across four labs shows.
Members of Google's Fairwind Program, its vetted cyber defenders, get a version without cyber guardrails. Google says it can "autonomously find, validate, and patch critical software vulnerabilities." Public API users get the guarded version.
As of October 14, 2026, Argon is live in the paid Gemini API, AI Studio, Android Studio, the Gemini app for AI Pro and Ultra subscribers, and Gemini for business on Google Cloud (Google Cloud, Gemini 4 Argon available on Google Cloud, 2026). Google Antigravity needs an AI Ultra plan.
ServiceNow reported "roughly 20% improved task success" over Gemini 3.8 Flash in early access. Before you build on it, plan around three limits:
The evidence in October 2026 says Argon is the best model for long-document knowledge work, but not for terminal coding or cheap general reasoning. It tops the independent Vals Index at 68.9% (Vals AI, Vals Index, 2026), yet trails Opus 5.5 by 9 points on Terminal-Bench 4.0. Don't switch wholesale. Route by workload.
| Workload | Our pick |
|---|---|
| Legal, finance, contract review | Gemini 4 Argon |
| Long documents and long video | Gemini 4 Argon |
| Large code migrations | Pilot Argon |
| Terminal agents, bug-fix loops | Claude Opus 5.5 or Sonnet 5.5 |
| General reasoning on a budget | Opus 5.5 (high) or GPT-6.1 Sol |
Our take: you've got about seven weeks of half-price testing. Use them on a pilot:
- Pick two Argon workloads from the table, with 50 to 100 real examples each.
- Run them on Argon and your current model, logging output tokens and accuracy.
- Price the results at December's $4/$20 rate.
- Switch only what still wins.
For more on the alternatives, see Claude Fable 5.1's cheaper cache and breaking changes and whether GPT-6 Astra lives up to OpenAI's AGI claim.
We'll estimate your real cost before you switch.
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