Gemini 4 Argon?
Here is what changed in Gemini 4 Argon, in plain terms:
- It can work much longer on one task. Argon supports a 1 million token unified input-output limit through the Gemini API. Earlier Gemini models were capped at 64K output tokens, so a single answer can now run far longer.
- It writes more directly. Google says Argon hedges less, repeats itself less, skips clichés, and follows style instructions better than earlier Gemini models.
- It handles documents and data well. Google highlights deep multimodal document comprehension and longitudinal data analysis. Isomorphic Labs cut a weeks-long patent and PDF review process down to hours.
Gemini 4 Argon pricing
| Plan | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Introductory price (until 30 Nov 2026) | $2 | $10 |
| Standard price (from 1 Dec 2026) | $4 | $20 |
Cached input tokens get a 95% discount.
Where you can use Gemini 4 Argon
- Everyday users: the Gemini app and AI Mode in Search, for Google AI Pro and Ultra subscribers.
- Developers: the Gemini API paid tier in AI Studio and Android Studio; Antigravity for Ultra subscribers.
- Enterprises: Gemini for business, the API, and Google Antigravity.
Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5: head-to-head
On Google's own comparison table, Gemini 4 Argon wins 13 of 19 rows, ties one and loses five. Independent testing is flatter: Artificial Analysis puts Argon level with GPT-6 Astra and behind Claude Opus 5.5.
Benchmark comparison
| Benchmark (what it tests) | Gemini 4 Argon | GPT-6 Astra | Claude Opus 5.5 |
|---|---|---|---|
| Vals Index (finance, legal, coding, tax work) | 68.9% | 63.1% | 67.0% |
| Automation Bench (business tasks end to end) | 51.3% | 41.4% | 42.5% |
| Vals Finance Agent v2 (financial research) | 65.4% | 53.5% | 58.6% |
| Harvey Legal Agent (legal tasks) | 19.6% | 5.4% | 3.8% |
| GraphWalks 256K-1M (very long documents) | 84.2% | 71.8% | 66.8% |
| DeepSWE v1.1 (real-world software engineering) | 77.9% | 74.1% | 74.2% |
| FrontierSWE v2 (hardest coding problems) | 55.0% | 65.5% | 62.3% |
| Terminal-Bench 4.0 (command-line agent work) | 57.4% | 58.2% | 66.4% |
| Artificial Analysis Intelligence Index (independent) | 53 | 53 | 58 |
Higher is better. Most scores are from Google's launch table and are vendor-reported; the Artificial Analysis index is independent.
Pricing and access
| Detail | Gemini 4 Argon | GPT-6 Astra | Claude Opus 5.5 |
|---|---|---|---|
| Input price (per 1M tokens) | $2 intro, $4 from 1 Dec 2026 | $10 | $4 |
| Output price (per 1M tokens) | $10 intro, $20 from 1 Dec 2026 | $50 | $20 |
| Cost per average task (Artificial Analysis) | $1.99 | $3.26 | $5.98 |
| Where to use it | Gemini app, AI Mode in Search, Gemini API, Antigravity | ChatGPT, OpenAI API | Claude apps, Claude API, AWS |
What the numbers actually say
- Argon is the knowledge-work leader. It wins all four business benchmarks, and outside parties (Vals AI, Zapier) keep score on those. The legal lead is big, but 19.6% is still a low absolute score, so it is not evidence that any model can do legal work unsupervised.
- Argon is best with very long inputs. On documents between 256K and 1M tokens, it holds 84.2% accuracy while rivals drop into the 60s and 70s. Think full annual reports, contract bundles or years of policy documents.
- Argon makes things up less often. On the AA-Omniscience test, Argon's hallucination rate is 15% against 51% for GPT-6 Astra. For tools that answer employee or customer questions, this matters more than a few reasoning points.
- Claude Opus 5.5 still leads terminal coding. It scores 66.4% on Terminal-Bench 4.0, nine points ahead of Argon, on a test of agents working in a command-line terminal. It also scores highest of the three on the Artificial Analysis Intelligence Index.
- GPT-6 Astra leads the hardest coding and computer-use tasks. It tops FrontierSWE v2 at 65.5% (10.5 points ahead of Argon) and leads OSWorld 2.0, which tests agents that operate desktop software (72.6% vs 69.2% for Argon; no Opus 5.5 score was reported).
- The low price has a catch. In Artificial Analysis's test run, Argon used 110M output tokens against a median of 82M for comparable models. At the standard 4/20 price from December, Emergent estimates its cost per task would roughly double to about $4 - above GPT-6 Astra's $3.26.
Which model should your team use?
For most finance, analytics, and operations teams, Gemini 4 Argon is now the model to test first - but keep Claude Opus 5.5 for coding agents and do not lock your workflows to any one vendor.
| If your team needs to... | Best fit today | Why |
|---|---|---|
| Do financial research and analysis | Gemini 4 Argon | 65.4% on Vals Finance Agent v2 vs 58.6% for Opus 5.5; also leads the Vals Index |
| Work across very long documents (contracts, annual reports, policy sets) | Gemini 4 Argon | 84.2% on GraphWalks 256K-1M vs 71.8% for the next best |
| Run multi-step business workflows end to end | Gemini 4 Argon | 51.3% on AutomationBench, 8.8 points ahead of Opus 5.5 |
| Build an internal Q&A or helpdesk bot | Gemini 4 Argon | 15% hallucination rate vs 51% for GPT-6 Astra (no Opus 5.5 figure published) |
| Run coding agents in a terminal | Claude Opus 5.5 | 66.4% on Terminal-Bench 4.0, 8.2 points ahead of the next best |
| Automate clicks across desktop software | GPT-6 Astra | 72.6% on OSWorld 2.0 vs 69.2% for Argon |
| Run high volumes cheaply | Gemini 4 Argon (until 30 Nov) | $1.99 per task vs $3.26 (Astra) and $5.98 (Opus 5.5), per Artificial Analysis |
What this means for Indian enterprise teams
At most large Indian companies, day-to-day work is not exotic AI research. It is month-end MIS, sales dashboards in Power BI, reconciliations in Excel, and processes that still move by email. Three practical takeaways:
1. Test on your own files, not on benchmarks. Take one real monthly report, one real contract, and one real dataset. Run the same prompt through all three models and compare accuracy and time saved.
2. Watch the token bill, not just the price tag. Argon is cheap per token but uses more tokens per task. Track cost per finished task during a two-week pilot, and recheck after the price change on 1 December 2026.
3. Keep the model swappable. Rankings have shifted with each new release, and Google says more Gemini 4 models are coming. Build automations so the model can be changed in one setting, not rebuilt from scratch.
The biggest gains still come from fixing the process first. An AI model on top of a messy, manual workflow just produces messy output faster.
Frequently asked questions
Is Gemini 4 Argon free?
No. Gemini 4 Argon needs a Google AI Pro or Ultra subscription in the Gemini app and AI Mode, or the paid tier of the Gemini API. Google's announcement lists only paid access, apart from a program offering selected scientists six months of free Antigravity access.
How much does Gemini 4 Argon cost?
The introductory API price is $2 per million input tokens and $10 per million output tokens, with cached input 95% off. From 1 December 2026, it rises to $4 and $20.
Is Gemini 4 Argon better than GPT-6?
On Google's table, Argon beats GPT-6 Astra on 14 of 19 rows and ties on one, especially in finance, legal, and long documents. GPT-6 Astra leads on the hardest coding tasks and computer use. On the independent Artificial Analysis index, the two are tied at 53.
Is Gemini 4 Argon better than Claude Opus 5.5?
For business knowledge work, yes - on Google's table Argon beats Opus 5.5 on 14 of the 18 rows where both have a score, including the Vals Index, finance and legal tests. Claude Opus 5.5 leads on terminal-based coding and scores higher on the independent Artificial Analysis index (58 vs 53).
When was Gemini 4 Argon released?
Google announced Argon on 30 September 2026 with early access for security partners, and made it available globally on 13 October 2026.
How do I use Gemini 4 Argon?
Select it in the Gemini app with a Google AI Pro or Ultra plan, or call the model gemini-4-argon on the paid Gemini API in Google AI Studio. Enterprises can also use it through Gemini for business.
Which AI model is best for Excel and data analysis?
For analysing data and financial reports, Gemini 4 Argon currently leads the finance and business-workflow benchmarks. For coding agents that work in a terminal, Claude Opus 5.5 scores higher on Terminal-Bench 4.0. No major benchmark tests Excel directly, so test both on your own files before you commit.
Conclusion
Gemini 4 Argon is a genuine step up for business knowledge work, and its introductory price makes this the right month to test it. But benchmarks are not your workflow. Claude Opus 5.5 and GPT-6 Astra still win specific jobs, and Google has said more Gemini 4 models are on the way.
The teams that benefit most will not be the ones that pick the "best" model. They will be the ones that redesign a process, pilot two models on real data, and measure hours saved.
Want to know which AI model fits your team's workflows? Awesome Analytics helps enterprise teams automate reporting, analysis, and business processes with Excel, Power BI, Python, and AI. Talk to us about an automation assessment or explore our corporate AI training programs.
