gpt-oss-120b (batch) Open vs 
Gemini 3.8 Flash Paid
gpt-oss-120b (batch) 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.
$0.6 /M outputNo per-token fees if you self-host
$3.75 /M outputManaged API (no infra to run)
gpt-oss-120b (batch) vs Gemini 3.8 Flash specs
| Spec | gpt-oss-120b (batch) | Gemini 3.8 Flash | Winner |
|---|---|---|---|
| Maker | OpenAI | – | |
| Type | Open-weight | Proprietary | |
| Context window | 131K tokens | 1M tokens | |
| Input price | $0.15/M · free self-host | $0.75/M | |
| Output price | $0.6/M · free self-host | $3.75/M | |
| Vision / multimodal | No | Yes | |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | |
| License | Apache 2.0 | Proprietary |
Price gap & when to choose each
gpt-oss-120b (batch) is ~6.3× cheaper than Gemini 3.8 Flash on output tokens ($0.6 vs $3.75 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 | gpt-oss-120b (batch) | Gemini 3.8 Flash |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✗ | ✓ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✗ | ✓ |
Benchmarks: gpt-oss-120b (batch) vs Gemini 3.8 Flash
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
How gpt-oss-120b (batch) and Gemini 3.8 Flash score
| Criterion | gpt-oss-120b (batch) | 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 | 3.5 | 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
gpt-oss-120b (batch) Open
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimize
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 gpt-oss-120b (batch) as good as Gemini 3.8 Flash?
gpt-oss-120b (batch) 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 gpt-oss-120b (batch) locally?
Yes. gpt-oss-120b (batch) 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 gpt-oss-120b (batch)?
gpt-oss-120b (batch) costs $0.6/M output vs $3.75/M for Gemini 3.8 Flash — roughly 6x cheaper via API, and free if you self-host.
gpt-oss-120b (batch) vs Gemini 3.8 Flash — which should I pick in 2026?
Choose gpt-oss-120b (batch) 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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