AI Models · Open-Source vs Paid

gpt-oss-120b (batch) Open vs Gemini 3.1 Pro Preview Paid

gpt-oss-120b (batch) vs Gemini 3.1 Pro Preview compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.

Prices & specs refreshed from live data · olud.ai

Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.

gpt-oss-120b (batch)OpenOpenAI
$0.15 /M input
$0.6 /M outputNo per-token fees if you self-host
TypeOpen-weight
Context window131K tokens
MultimodalNo
Self-hostYes
Gemini 3.1 Pro PreviewPaidGoogle
$2 /M input
$12 /M outputManaged API (no infra to run)
TypeProprietary
Context window1M tokens
MultimodalYes
Self-hostNo
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 3.1 Pro Preview if you want frontier capability through a managed API with zero infrastructure to run.

gpt-oss-120b (batch) vs Gemini 3.1 Pro Preview specs

Specgpt-oss-120b (batch)Gemini 3.1 Pro PreviewWinner
MakerOpenAIGoogle
TypeOpen-weightProprietarygpt-oss-120b (batch)
Context window131K tokens1M tokensGemini 3.1 Pro Preview
Input price$0.15/M · free self-host$2/Mgpt-oss-120b (batch)
Output price$0.6/M · free self-host$12/Mgpt-oss-120b (batch)
Vision / multimodalNoYesGemini 3.1 Pro Preview
Tool / function callingYesYes= Tie
Self-hostableYesNo (API only)gpt-oss-120b (batch)
LicenseApache 2.0Proprietarygpt-oss-120b (batch)

Price gap & when to choose each

20×cheaper per output token

gpt-oss-120b (batch) is ~20× cheaper than Gemini 3.1 Pro Preview on output tokens ($0.6 vs $12 per M tokens).

Choose gpt-oss-120b (batch) if…
  • 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
Choose Gemini 3.1 Pro Preview if…
  • 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

Capabilitygpt-oss-120b (batch)Gemini 3.1 Pro Preview
Open weights (downloadable)
Self-hostable
Runs fully offline
Vision / multimodal
Tool / function calling
1M+ context window

Benchmarks: gpt-oss-120b (batch) vs Gemini 3.1 Pro Preview

Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).

gpt-oss-120b (batch) delivers 6.9× more intelligence per dollar.
gpt-oss-120b (batch)
Gemini 3.1 Pro Preview
Intelligence index
12.3
30.4
Coding index
30.4
68.8
Math index
93.4
GPQA
78.2%
94.1%
MMLU-Pro
80.8%
Humanity's Last Exam
19.6%
47%
Long Context Reasoning
52%
82%
LiveCodeBench
87.8%
SciCode
34%
58.7%
AIME 2025
93.4%
IFBench
69%
77.1%
τ²-Bench
65.8%
95.6%
τ-Bench Banking
12.8%
21.4%
Terminal-Bench
26.2%
73.8%
Terminal-Bench Hard
23.5%
53.8%
Speed
179 tok/s
125.2 tok/s
Latency
0.51s
24.48s
Intelligence per $
47.1
6.8

Benchmark data by Artificial Analysis.

How gpt-oss-120b (batch) and Gemini 3.1 Pro Preview score

🏆 Best value & openness: gpt-oss-120b (batch) (4.4 vs 3.2 / 5)
Criteriongpt-oss-120b (batch)Gemini 3.1 Pro Preview
Cost-efficiency5.03.5
Context window3.55.0
Openness5.01.5
Self-hosting5.01.0
Multimodality3.55.0

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

OpenAI · Open-weight

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 3.1 Pro Preview Paid

Google · Proprietary

Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation.

Other models in these families

These variants are tracked but not compared here — one page per family keeps the comparison readable.

Other variants tracked
gpt-oss-120bgpt-oss-20b (batch)gpt-oss-20bgpt-oss-safeguard-20b
Other variants tracked
Gemini 3.1 Pro Preview (batch)Gemini 2.5 ProGemini 2.5 Pro (batch)Gemini 2.5 Pro Preview 05-06Gemini 3.1 Pro Preview Custom ToolsNano Banana Pro (Gemini 3 Pro Image Preview)

Frequently asked questions

Is gpt-oss-120b (batch) as good as Gemini 3.1 Pro Preview?

gpt-oss-120b (batch) is open-weight and competitive on many tasks, but Gemini 3.1 Pro Preview 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 3.1 Pro Preview 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 $12/M for Gemini 3.1 Pro Preview — roughly 20x cheaper via API, and free if you self-host.

gpt-oss-120b (batch) vs Gemini 3.1 Pro Preview — 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 3.1 Pro Preview if you want frontier capability through a managed API with zero infrastructure to run.

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