INLing 3.0 Flash VL Open vs 
Gemini 3.8 Flash Paid
Ling 3.0 Flash VL vs Gemini 3.8 Flash compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
Gemini 3.8 Flash — full profile ›
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
$0.18 /M outputNo per-token fees if you self-host
$4.13 /M outputManaged API (no infra to run)
Ling 3.0 Flash VL vs Gemini 3.8 Flash specs
| Spec | Ling 3.0 Flash VL | Gemini 3.8 Flash | Winner |
|---|---|---|---|
| Maker | Inclusionai | – | |
| Type | Open-weight | Proprietary | INLing 3.0 Flash VL |
| Context window | 131K tokens | 1M tokens | |
| Input price | $0.06/M · free self-host | $0.83/M | INLing 3.0 Flash VL |
| Output price | $0.18/M · free self-host | $4.13/M | INLing 3.0 Flash VL |
| Vision / multimodal | Yes | Yes | = Tie |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | INLing 3.0 Flash VL |
| License | Open weights | Proprietary | INLing 3.0 Flash VL |
Price gap & when to choose each
Ling 3.0 Flash VL is ~23× cheaper than Gemini 3.8 Flash on output tokens ($0.18 vs $4.13 per M tokens).
- You want to self-host or run on your cloud
- You prioritize data privacy & control
- You want the lowest operating costs
- You are building open or reproducible AI
- You want frontier performance through a managed API
- You need the longer 1M context window
- You value reliability & ecosystem
- You don't want to manage infrastructure
Feature comparison
| Capability | Ling 3.0 Flash VL | Gemini 3.8 Flash |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✓ | ✓ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✗ | ✓ |
Benchmarks: Ling 3.0 Flash VL vs Gemini 3.8 Flash
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
How Ling 3.0 Flash VL and Gemini 3.8 Flash score
| Criterion | Ling 3.0 Flash VL | Gemini 3.8 Flash |
|---|---|---|
| Cost-efficiency | 5.0 | 4.0 |
| Context window | 3.5 | 5.0 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 5.0 | 5.0 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
What each model is
Ling 3.0 Flash VL Open
Ling 3.0 Flash VL builds on Ling 3.0 Flash (124B total / 5.5B active MoE from InclusionAI), further strengthening its language capabilities while adding native visual perception and advanced visual...
Gemini 3.8 Flash Paid
Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.
Other models in these families
These variants are tracked but not compared here — one page per family keeps the comparison readable.
Frequently asked questions
Is Ling 3.0 Flash VL as good as Gemini 3.8 Flash?
Ling 3.0 Flash VL is open-weight and competitive on many tasks, but Gemini 3.8 Flash may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Can I run Ling 3.0 Flash VL locally?
Yes. Ling 3.0 Flash VL has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Gemini 3.8 Flash is API-only and cannot be self-hosted.
How much cheaper is Ling 3.0 Flash VL?
Ling 3.0 Flash VL costs $0.18/M output vs $4.13/M for Gemini 3.8 Flash — roughly 23x cheaper via API, and free if you self-host.
Ling 3.0 Flash VL vs Gemini 3.8 Flash — which should I pick in 2026?
Choose Ling 3.0 Flash VL if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Gemini 3.8 Flash if you want frontier capability through a managed API with zero infrastructure to run.
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