Gemini 4 Argon vs Claude Opus 5.5
Gemini 4 Argon vs Claude Opus 5.5: official per-token prices (Argon is about half of Opus), ten benchmarks from Google's model card, 1M context and multimodal differences, and which to pick for coding agents, long documents and video. Prices on this page come live from the CallAI gateway.
Gemini 4 Argon
Claude Opus 5.5
For coding, terminal agents and multi-step changes in large repos, pick Claude Opus 5.5 — it leads Terminal-bench 4.0 and FrontierSWE v2. For long documents and video at hundreds of thousands to a million tokens, or finance and legal knowledge-work agents, pick Gemini 4 Argon — it scores higher there at half the price. Both run on one CallAI key, so you can route per task.
Benchmarks from Google's model card (vendor-reported)
| Benchmark | Gemini 4 Argon | Claude Opus 5.5 | Winner | Notes |
|---|---|---|---|---|
| DeepSWE v1.1 | 77.9% | 74.2% | Gemini 4 Argon | Real-codebase engineering tasks |
| FrontierSWE v2 | 55.0% | 62.3% | Claude Opus 5.5 | Hard software engineering |
| Terminal-bench 4.0 | 57.4% | 66.4% | Claude Opus 5.5 | Terminal agent tasks; Anthropic's own table also reports 66.4% |
| AutomationBench | 51.3% | 42.5% | Gemini 4 Argon | Knowledge-work automation |
| Vals Finance Agent v2 | 65.4% | 58.6% | Gemini 4 Argon | Finance analyst agent |
| PostTrainBench | 45.3% | 49.3% | Claude Opus 5.5 | ML engineering |
| Terminal-Bench Science 0.1 | 57.6% | 63.3% | Claude Opus 5.5 | Scientific terminal tasks |
| GraphWalks 256K–1M (BFS F1) | 84.2% | 66.8% | Gemini 4 Argon | Very long context reasoning — the widest gap in the table |
| LVBench | 91.7% | 83.7% | Gemini 4 Argon | Long video understanding |
| Agent's Last Exam | 39.5% | 38.2% | Tie | Computer-use agents, within 2 points |
Gemini 4 Argon strengths & weaknesses
- About half the list price of Opus 5.5 ($2 / $10 vs $4 / $20 per 1M tokens)
- Leads very long context reasoning: GraphWalks 256K–1M 84.2% vs 66.8%
- Accepts text, image, video, audio and PDF; LVBench long-video score 91.7%
- Stronger knowledge-work agents: AutomationBench 51.3%, Vals Finance Agent v2 65.4%
- Text output only — no image or audio generation
- Implicit caching only; explicit caching is not supported
- Behind on terminal agents and hard engineering: Terminal-bench 4.0 57.4%, FrontierSWE v2 55.0%
- Computer use is still in preview
Claude Opus 5.5 strengths & weaknesses
- Terminal-bench 4.0 at 66.4%, the top score in Google's table
- Leads Argon on FrontierSWE v2 (62.3%) and PostTrainBench (49.3%)
- Anthropic positions it for multi-step changes in large codebases and long-horizon agents
- Cache reads at $0.20 / 1M make repeated long system prompts much cheaper
- Twice the list price of Argon
- Clearly behind on 1M-scale context reasoning (GraphWalks 256K–1M 66.8%)
- Lower scores on knowledge-work automation and finance agents
Scenario recommendations
| Scenario | Pick | Why |
|---|---|---|
| Terminal agents and multi-file refactors in Claude Code / Cursor | Claude Opus 5.5 | 9 points higher on Terminal-bench 4.0 and 7.3 points higher on FrontierSWE v2. |
| Whole contracts, filings or codebases in one context | Gemini 4 Argon | Leads 256K–1M context reasoning by more than 17 points at half the price. |
| Video and audio analysis | Gemini 4 Argon | Native video and audio input; LVBench 91.7% vs 83.7%. |
| Budget-sensitive batch work | Gemini 4 Argon | The same token volume costs about half at list prices. |
Python dual-model gateway sample
OpenAI-compatibleOne Base URL and API key — switch models with the model parameter only:
import os
from openai import OpenAI
# One CallAI key — route freely between Gemini 4 Argon and Claude Opus 5.5
client = OpenAI(
api_key="sk-live-your-callai-key",
base_url="https://api.callaiapi.com/v1"
)
def query_model(model_id: str, prompt: str):
response = client.chat.completions.create(
model=model_id,
messages=[{"role": "user", "content": prompt}],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="", flush=True)
print("\n" + "="*40 + "\n")
# Model 1: Gemini 4 Argon
print(">>> Output from: Gemini 4 Argon")
query_model("gemini-4-argon", "Briefly analyze the trade-offs of this architecture")
# Model 2: Claude Opus 5.5
print(">>> Output from: Claude Opus 5.5")
query_model("claude-opus-5.5", "Briefly analyze the trade-offs of this architecture")Sources
FAQ
How much do Gemini 4 Argon and Claude Opus 5.5 cost per token?
List prices as of 2026-10-22: Gemini 4 Argon is $2 input and $10 output per 1M tokens; Claude Opus 5.5 is $4 input and $20 output per 1M tokens. The cards at the top of this page show the current CallAI price, updated from upstream automatically.
Who ran these benchmarks?
The numbers come from Google DeepMind's Gemini 4 Argon model card (2026-10-13) and are vendor-reported, including the Opus 5.5 column. Terminal-bench 4.0 at 66.4% matches Anthropic's own launch table. No independent verification yet.
How do I call Gemini 4 Argon and Claude Opus 5.5 through one API?
Create a key in the CallAI console, set the OpenAI SDK base_url to https://api.callaiapi.com/v1 and use the model name shown on the cards. Pay as you go from $1, with crypto and card payments; balance never expires.