Neither wins outright: Google: Gemini 4 Argon (batch) is the pick when you need file / PDF input, Qwen: Qwen3.8 Omni Flash when cost matters at scale.
| Spec | Google: Gemini 4 Argon (batch) | Qwen: Qwen3.8 Omni Flash |
|---|---|---|
| Provider | Qwen | |
| Input price (per 1M tokens) | $1.00 | $0.15 |
| Output price (per 1M tokens) | $5.00 | $0.47 |
| Blended price (3:1 input/output) | $2.00 | $0.23 |
| Cheaper than … of all models we track | 25% | 67% |
| Context window | 1.0M tokens (~1,573 pages) | 1M tokens (~1,500 pages) |
| Larger context than … of all models | 81% | 69% |
| Max output per response | 944K tokens (~707,800 words) | 131K tokens (~98,300 words) |
| Input types | audio, file, image, text, video | text, image, audio, video |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2026-10-13 | 2026-09-21 |
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) | Qwen: Qwen3.8 Omni Flash | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $175 | $20.65 | Qwen: Qwen3.8 Omni Flash saves $154 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $170 | $22.70 | Qwen: Qwen3.8 Omni Flash saves $147 |
| 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 | $23.64 | Qwen: Qwen3.8 Omni Flash saves $156 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $110 | $14.82 | Qwen: Qwen3.8 Omni Flash saves $95.18 |
| 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 | $7.97 | Qwen: Qwen3.8 Omni Flash saves $47.03 |
| Capability | Google: Gemini 4 Argon (batch) | Qwen: Qwen3.8 Omni Flash | 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 | — | accept documents without your own parsing step |
| Image output | — | — | generate images, not just text |
| Built-in web search | ✓ Yes | — | answer from live web results |
| Seed (reproducible sampling) | ✓ Yes | ✓ Yes | repeat a generation for tests and evals |
| Log-probabilities | — | ✓ Yes | read token confidence for classification and scoring |
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Neither wins outright: Google: Gemini 4 Argon (batch) is the pick when you need file / PDF input, Qwen: Qwen3.8 Omni Flash when cost matters at scale. In short: Google: Gemini 4 Argon (batch) when you need file / PDF input, you need built-in web search, you generate long outputs in one go; Qwen: Qwen3.8 Omni Flash when cost matters at scale, you need log-probabilities.
Qwen: Qwen3.8 Omni Flash is cheaper: $0.23 vs $2.00 per 1M tokens at a typical 3:1 input/output mix, 89% less. For 50,000 support conversations a month that is $20.65 against $175.
Google: Gemini 4 Argon (batch) costs $1.00 per 1M input tokens and $5.00 per 1M output tokens; Qwen: Qwen3.8 Omni Flash costs $0.15 input and $0.47 output. Output tokens are billed separately, so long answers shift the gap.
Google: Gemini 4 Argon (batch): 1.0M tokens, about 1,573 pages, against 1M (about 1,500 pages) for Qwen: Qwen3.8 Omni Flash.
Tool calling — Google: Gemini 4 Argon (batch): yes, Qwen: Qwen3.8 Omni Flash: yes. JSON-schema structured output — Google: Gemini 4 Argon (batch): yes, Qwen: Qwen3.8 Omni Flash: 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; Qwen: Qwen3.8 Omni Flash up to 131K (about 98,300 words).
Google: Gemini 4 Argon (batch) was listed on 2026-10-13; Qwen: Qwen3.8 Omni Flash on 2026-09-21.
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 →Qwen3.8 Omni Flash is an omni-modal reasoning model from Alibaba, the first Qwen model built around agentic capabilities with native audio-video understanding. It is suited for audio-video analysis and summarization,...
Full Qwen: Qwen3.8 Omni Flash specs →