Gemini 4 Argon vs Claude Opus 5.5 for coding agents

Originally published on the QuickSilver Pro blog.

Gemini 4 Argon is Google's new flagship reasoning model. On QuickSilver Pro it costs $2.00 / $10.00 per million tokens, against $1.00 / $5.00 for Claude Opus 5.5. We wanted to know whether it can stand in for Opus in a coding agent, and what that costs.

We tested this on 2026-10-27. Both models ran in the same agent, OpenCode, on six small but real coding tasks, and we graded every run with tests the agent never saw. Short answer: both passed every hidden test in every run. Gemini 4 Argon was slower and more expensive on every task: 134 seconds and $0.142 per task on average, against 82 seconds and $0.094 for Opus.

opencode run --auto), with QuickSilver Pro added as an OpenAI-compatible provider. The only thing that changed between runs was -m quicksilverpro/gemini-4-argon or -m quicksilverpro/claude-opus-5-5. QuickSilver Pro serves Gemini 4 Argon on the OpenAI-compatible endpoints (/v1/chat/completions and /v1/responses), not on the Anthropic Messages API that Claude Code needs. So we used one agent that can drive both models. The tasks:

"1h30m", "2 hours and 5 min", "PT1H30M") with a page of rules on separators, unit order and what must be rejected, including non-ASCII digits. 84 hidden tests.${section.key} references with cycle detection. 57 hidden tests. Averages over two runs per model and task. Cost is per run, at QuickSilver Pro prices on 2026-10-27.

Where the tokens and the money went, averaged over all 12 runs per model:

Quality was a tie. Neither model failed a hidden test in 24 runs. That includes all 136 cases of the official Mustache suite and the 316 comparisons with the original function in the refactor.

Gemini got there with less checking. On the refactor, both Opus runs saved a copy of the original function and compared old and new output on random inputs before finishing. Neither Gemini run did that; Gemini relied on the one visible test and still matched the original on all 316 hidden comparisons. On the Mustache task, both Opus runs downloaded the official spec test files from GitHub and ran them. Our hidden tests for that task are the same files, so Opus's score there is not independent. Gemini did not download them and passed all 136 anyway. What Gemini did instead was run the test suite more often: 24 pytest runs over its 12 runs, against 14 for Opus.

Gemini was slower on every task, from 1.2 times as long on the Mustache renderer to 2.3 times on the config parser. Its typical request was actually quicker than Opus's, with a median of 6.2 seconds against 7.6. But it made more requests (10.2 per task against 7.9), and its slow requests were slower: one in ten took over 34 seconds, and the slowest took 109. The config parser shows the effect most: the second Gemini run took 18 requests and almost five minutes.

Gemini cost more on every task, about 1.5 times as much on average. It has the higher per-token prices on QuickSilver Pro, and it sent more input per task because it made more requests. It wrote fewer output tokens than Opus, but at its output price they still cost more ($0.056 against $0.039 per task). Gemini's caching did better than Opus's: 69% of its input came from the prompt cache. On every Opus request the cache covered the same 8,028 tokens, about the size of OpenCode's system prompt and tool definitions. The rest of the conversation was billed at the full input rate. Even so, Opus came out cheaper.

On these tasks, Opus 5.5 is the better choice for a coding agent. It solved the same tasks, in about 60% of the time, for about two thirds of the cost.

Gemini 4 Argon solved every task too, so it is a workable second model. Use it if you want a non-Anthropic model in the loop, for example to cross-check a change, or if you are already building on Gemini elsewhere. We did not find a task in this set that either model failed, so we cannot tell you which one solves more on harder work. On this evidence, Gemini 4 Argon does not save you money over Opus 5.5.

The OpenCode config we used, pointed at the public endpoint. Save it as opencode.json in your project:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "quicksilverpro": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "QuickSilver Pro",
      "options": {
        "baseURL": "https://api.quicksilverpro.io/v1",
        "apiKey": "{env:QSP_API_KEY}"
      },
      "models": {
        "gemini-4-argon": { "name": "Gemini 4 Argon", "limit": { "context": 1000000, "output": 64000 } },
        "claude-opus-5-5": { "name": "Claude Opus 5.5", "limit": { "context": 1000000, "output": 64000 } }
      }
    }
  }
}

Then run the same prompt with each model:

export QSP_API_KEY=YOUR_QSP_KEY

opencode run --auto -m quicksilverpro/gemini-4-argon "Fix the failing test in tests/"
opencode run --auto -m quicksilverpro/claude-opus-5-5 "Fix the failing test in tests/"

--auto approves the agent's file edits and shell commands without asking, so use it only in a project you can reset. OpenCode shows a cost of 0 for a custom provider. The real cost of each request, with its model and token counts, is in your QuickSilver Pro dashboard. Model details are on the Gemini 4 Argon and Claude Opus 5.5 pages. For the OpenCode setup, see Use QuickSilver Pro in OpenCode and Crush. For Opus against GPT-6.1 Sol on the same tasks, see Claude Opus 5.5 vs GPT-6.1 Sol for coding agents.