gpt-oss-120B vs Gemini 3 Pro: Open-Weight MoE vs Frontier API on Cost, Context and Tool Use (2026 Decision Guide)

Last updated: November 5, 2026

gpt-oss-120b vs Gemini 3 Pro comes down to control versus reach. gpt-oss-120b is an open-weight model you can run anywhere; on AkashML it costs $0.037 per million input tokens and $0.187 per million output tokens (as of November 5, 2026). Gemini 3 Pro is a closed Google API at $2 and $12 that adds a 1M-token context, multimodal input and higher benchmark scores.

TL;DR

• Price: gpt-oss-120b on AkashML is about 54x cheaper on input and 64x cheaper on output than Gemini 3 Pro (November 5, 2026 list prices).
• Context: 131,072 tokens for gpt-oss-120b vs 1,048,576 for Gemini 3 Pro.
• Tool use: Both do native function calling with low, medium and high reasoning settings. Gemini 3 Pro scores higher on agent benchmarks.
• Ownership: gpt-oss-120b is Apache 2.0 and fits on one 80 GB GPU. Gemini 3 Pro runs only on Google.
• Naming: Google retired the gemini-3-pro-preview ID on March 9, 2026; it now routes to Gemini 3.1 Pro Preview at the same price.

Which should you choose: gpt-oss-120b or Gemini 3 Pro?

Choose gpt-oss-120b when cost per task, provider choice and data control matter most; choose Gemini 3 Pro when a request needs more than 128K tokens, non-text input, or maximum accuracy.

How do gpt-oss-120b and Gemini 3 Pro compare on specs?

gpt-oss-120b is a 117B-parameter Mixture-of-Experts (MoE) model that activates 5.1B parameters per token; Google does not disclose Gemini 3 Pro’s size.

The gap that matters most is context: Gemini 3 Pro takes about 8x more tokens per request. On memory, gpt-oss-120b’s MXFP4 weights are about 65 GB, which is why one 80 GB GPU is enough before accounting for the KV cache. For how total and active parameters translate into GPU memory, see our MoE GPU sizing guide.

Sources: OpenAI, Introducing gpt-oss; Hugging Face: openai/gpt-oss-120b; Ollama: gpt-oss:120b; Gemini 3 developer guide.

How much does gpt-oss-120b cost compared with Gemini 3 Pro?

On AkashML, gpt-oss-120b costs $0.037 input and $0.187 output per million tokens; Gemini 3 Pro costs $2.00 and $12.00 for prompts up to 200K tokens. List rates as of November 5, 2026:

On that monthly agent workload, gpt-oss-120b on AkashML is about 59x cheaper than Gemini 3 Pro Standard and 30x cheaper than Batch. Both models bill reasoning tokens as output, so high reasoning effort raises gpt-oss-120b’s token count, and different tokenizers mean totals won’t match exactly.

Sources: AkashML pricing; Gemini API pricing.

Is gpt-oss-120b as good as Gemini 3 Pro at tool use?

No: Gemini 3 Pro leads on every benchmark in the table below, but gpt-oss-120b handles structured tool calling well at a fraction of the cost. OpenAI says gpt-oss-120b matches or beats its o4-mini on TauBench, a tool-calling benchmark.

Figures are vendor-reported. OpenAI used τ-bench and Google used the newer τ²-bench, so treat the first row as directional.

Sources: gpt-oss model card; Google DeepMind: Gemini 3.1 Pro.

When is Gemini 3 Pro the right call?

Gemini 3 Pro is the right call when a task needs more than 128K tokens at once, needs non-text input, or is hard enough that accuracy outweighs cost.

• Long context: a full monorepo or hours of transcripts in one prompt, no chunking.
• Multimodal input: screenshots, scanned forms, audio and video.

The tradeoff is lock-in. Google sets the price and the deprecation schedule, and the original Gemini 3 Pro ID was retired about 16 weeks after launch (Gemini API deprecations).

Where can you run gpt-oss-120b?

You can call gpt-oss-120b through any hosted API that serves it, through model routers such as OpenRouter, or self-host the Apache 2.0 weights on one 80 GB GPU such as an NVIDIA H100. On AkashML it runs behind an OpenAI-compatible endpoint that supports tools, tool_choice, response_format and reasoning_effort, so existing OpenAI SDK code needs only a new base URL, API key and model name:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.akashml.com/v1",
    api_key=os.environ["AKASHML_API_KEY"],
)

tools = [{
    "type": "function",
    "function": {
        "name": "get_order",
        "description": "Look up an order's shipping status by order ID",
        "parameters": {
            "type": "object",
            "properties": {"order_id": {"type": "string"}},
            "required": ["order_id"],
        },
    },
}]

response = client.chat.completions.create(
    model="openai/gpt-oss-120b",
    reasoning_effort="medium",
    messages=[{"role": "user", "content": "When does order 4417 ship?"}],
    tools=tools,
)

for call in response.choices[0].message.tool_calls or []:
    print(call.function.name, call.function.arguments)

AkashML is also listed as a gpt-oss-120b provider on OpenRouter. Teams that need dedicated, reserved GPU capacity can use Akash Enterprise.

If gpt-oss-120b falls short but you want to stay open, step up to a larger open model first: GLM 5.3 on AkashML (753B MoE, 1M context) costs $0.19/$4.40 per million tokens.

Try it: test gpt-oss-120b in the AkashML Playground, or create an account and follow the quick start to make your first API call.

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