Neither wins outright: Google: Gemma 4 31B (batch) is the pick when you need structured output (JSON schema), inclusionAI: Ling 3.0 Flash Fin (free) when cost matters at scale.
| Spec | Google: Gemma 4 31B (batch) | inclusionAI: Ling 3.0 Flash Fin (free) |
|---|---|---|
| Provider | Inclusionai | |
| Input price (per 1M tokens) | $0.39 | $0 |
| Output price (per 1M tokens) | $0.97 | $0 |
| Blended price (3:1 input/output) | $0.53 | $0 |
| Cheaper than … of all models we track | 52% | 90% |
| Context window | 262K tokens (~393 pages) | 262K tokens (~393 pages) |
| Larger context than … of all models | 47% | 47% |
| Max output per response | 236K tokens (~176,900 words) | 33K tokens (~24,600 words) |
| Input types | image, text, video | text |
| Output types | text | text |
| Free variant | No | Yes |
| Listed since | 2026-04-02 | 2026-08-27 |
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: Gemma 4 31B (batch) | inclusionAI: Ling 3.0 Flash Fin (free) | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $48.65 | $0 | inclusionAI: Ling 3.0 Flash Fin (free) saves $48.65 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $56.50 | $0 | inclusionAI: Ling 3.0 Flash Fin (free) saves $56.50 |
| 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) | $58.44 | $0 | inclusionAI: Ling 3.0 Flash Fin (free) saves $58.44 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $37.02 | $0 | inclusionAI: Ling 3.0 Flash Fin (free) saves $37.02 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $20.47 | $0 | inclusionAI: Ling 3.0 Flash Fin (free) saves $20.47 |
| Capability | Google: Gemma 4 31B (batch) | inclusionAI: Ling 3.0 Flash Fin (free) | What it lets you do |
|---|---|---|---|
| Tool / function calling | ✓ Yes | ✓ Yes | call your APIs and run agent loops |
| Structured output (JSON schema) | ✓ Yes | — | return JSON that validates against your schema |
| Reasoning / thinking mode | ✓ Yes | ✓ Yes | spend extra tokens thinking before answering hard problems |
| Image input | ✓ Yes | — | read screenshots, charts and scanned pages |
| Audio input | — | — | take speech or audio directly |
| File / PDF input | — | — | accept documents without your own parsing step |
| Image output | — | — | generate images, not just text |
| Built-in web search | — | — | answer from live web results |
| Seed (reproducible sampling) | — | ✓ 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: Gemma 4 31B (batch) is the pick when you need structured output (JSON schema), inclusionAI: Ling 3.0 Flash Fin (free) when cost matters at scale. In short: Google: Gemma 4 31B (batch) when you need structured output (JSON schema), you need image input, you generate long outputs in one go; inclusionAI: Ling 3.0 Flash Fin (free) when cost matters at scale, you need seed (reproducible sampling), you need log-probabilities, you want to prototype at zero cost, you want the newer model.
inclusionAI: Ling 3.0 Flash Fin (free) is cheaper: $0 vs $0.53 per 1M tokens at a typical 3:1 input/output mix, 100% less. For 50,000 support conversations a month that is $0 against $48.65.
Google: Gemma 4 31B (batch) costs $0.39 per 1M input tokens and $0.97 per 1M output tokens; inclusionAI: Ling 3.0 Flash Fin (free) costs $0 input and $0 output. Output tokens are billed separately, so long answers shift the gap.
Both take 262K tokens per request — roughly 393 pages of text.
Tool calling — Google: Gemma 4 31B (batch): yes, inclusionAI: Ling 3.0 Flash Fin (free): yes. JSON-schema structured output — Google: Gemma 4 31B (batch): yes, inclusionAI: Ling 3.0 Flash Fin (free): not advertised.
Only Google: Gemma 4 31B (batch) accepts image input; inclusionAI: Ling 3.0 Flash Fin (free) is text-only for input.
inclusionAI: Ling 3.0 Flash Fin (free) has a free variant (inclusionai/ling-3.0-flash-fin:free). Free variants usually carry tighter rate limits.
Google: Gemma 4 31B (batch) can write up to 236K tokens (about 176,900 words) per response; inclusionAI: Ling 3.0 Flash Fin (free) up to 33K (about 24,600 words).
Google: Gemma 4 31B (batch) was listed on 2026-04-02; inclusionAI: Ling 3.0 Flash Fin (free) on 2026-08-27.
Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...
Full Google: Gemma 4 31B (batch) specs →Ling 3.0 Flash Fin is a finance-focused mixture-of-experts model from InclusionAI, built on Ling 3.0 Flash with 5.1B active parameters out of 124B total. It is designed for real-world investment...
Full inclusionAI: Ling 3.0 Flash Fin (free) specs →