Google: Gemini 4 Argon (batch) comes out ahead on the data we track: 2 advantages and none for Meta: Muse Wonder.
| Spec | Google: Gemini 4 Argon (batch) | Meta: Muse Wonder |
|---|---|---|
| Provider | Meta | |
| Input price (per 1M tokens) | $1.00 | $4.00 |
| Output price (per 1M tokens) | $5.00 | $20.00 |
| Blended price (3:1 input/output) | $2.00 | $8.00 |
| Cheaper than … of all models we track | 25% | 7% |
| Context window | 1.0M tokens (~1,573 pages) | 1.0M tokens (~1,573 pages) |
| Larger context than … of all models | 81% | 81% |
| Max output per response | 944K tokens (~707,800 words) | 944K tokens (~707,800 words) |
| Input types | audio, file, image, text, video | text, image, video, file, audio |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2026-10-13 | 2026-10-12 |
Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
| Workload (per month) | Google: Gemini 4 Argon (batch) | Meta: Muse Wonder | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $175 | $700 | Google: Gemini 4 Argon (batch) saves $525 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $170 | $680 | Google: Gemini 4 Argon (batch) saves $510 |
| Coding / tool-using agent 2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens) | $180 | $720 | Google: Gemini 4 Argon (batch) saves $540 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $110 | $440 | Google: Gemini 4 Argon (batch) saves $330 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $55.00 | $220 | Google: Gemini 4 Argon (batch) saves $165 |
| Capability | Google: Gemini 4 Argon (batch) | Meta: Muse Wonder | What it lets you do |
|---|---|---|---|
| Tool / function calling | ✓ Yes | ✓ Yes | call your APIs and run agent loops |
| Structured output (JSON schema) | ✓ Yes | ✓ Yes | return JSON that validates against your schema |
| Reasoning / thinking mode | ✓ Yes | ✓ Yes | spend extra tokens thinking before answering hard problems |
| Image input | ✓ Yes | ✓ Yes | read screenshots, charts and scanned pages |
| Audio input | ✓ Yes | ✓ Yes | take speech or audio directly |
| File / PDF input | ✓ Yes | ✓ Yes | accept documents without your own parsing step |
| Image output | — | — | generate images, not just text |
| Built-in web search | ✓ Yes | ✓ Yes | answer from live web results |
| Seed (reproducible sampling) | ✓ Yes | — | repeat a generation for tests and evals |
| Log-probabilities | — | — | read token confidence for classification and scoring |
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
No advantage on the data we track — pick it for provider preference or output quality on your own prompts.
Google: Gemini 4 Argon (batch) comes out ahead on the data we track: 2 advantages and none for Meta: Muse Wonder.
Google: Gemini 4 Argon (batch) is cheaper: $2.00 vs $8.00 per 1M tokens at a typical 3:1 input/output mix, 75% less. For 50,000 support conversations a month that is $175 against $700.
Google: Gemini 4 Argon (batch) costs $1.00 per 1M input tokens and $5.00 per 1M output tokens; Meta: Muse Wonder costs $4.00 input and $20.00 output. Output tokens are billed separately, so long answers shift the gap.
Both take 1.0M tokens per request — roughly 1,573 pages of text.
Tool calling — Google: Gemini 4 Argon (batch): yes, Meta: Muse Wonder: yes. JSON-schema structured output — Google: Gemini 4 Argon (batch): yes, Meta: Muse Wonder: yes.
Yes, both accept image input.
No free variant is listed for either; both are pay-per-token.
Google: Gemini 4 Argon (batch) can write up to 944K tokens (about 707,800 words) per response; Meta: Muse Wonder up to 944K (about 707,800 words).
Google: Gemini 4 Argon (batch) was listed on 2026-10-13; Meta: Muse Wonder on 2026-10-12.
Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across...
Full Google: Gemini 4 Argon (batch) specs →Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with...
Full Meta: Muse Wonder specs →