Benchmarks

GPT-OSS 120B vs Gemini 3 Pro: Benchmarks & Cost

2026-09-02·18 min read·LLMPodium Team

### Quick Answer: GPT-OSS 120B vs Gemini 3 Pro

Gemini 3 Pro dominates complex multimodal reasoning and massive 2M-token agentic contexts, outperforming on HLE (32.4%) and GPQA Diamond (79.2%). OpenAI's open-weights GPT-OSS 120B (117B total, 24B active MoE) wins on data sovereignty, zero token egress fees, and sub-15ms local latency on dual H100s via vLLM FP8.

1. Executive Summary: Open-Weights MoE Meets Proprietary Frontier

In 2026, the frontier AI landscape reached an inflection point. OpenAI released GPT-OSS 120B, its flagship open-weights Mixture-of-Experts (MoE) foundation model, directly challenging Google's closed megasystem, Gemini 3 Pro, and its high-efficiency counterpart, Gemini 2.5 Flash. Simultaneously, enterprise teams evaluate both against GPT 5.2, OpenAI's closed flagship.

The core tension is no longer raw intelligence alone—it is the operational trade-off between complete model sovereignty (self-hosting, weights inspection, zero privacy leakage, deterministic latency) and hyper-scaled proprietary infrastructure (2M+ native multimodal context, dynamic test-time compute, and zero cluster maintenance). 

+-----------------------------------------------------------------------------------------+
|                              ARCHITECTURAL PARADIGMS IN 2026                            |
+-----------------------------------------------------------------------------------------+
|                                                                                         |
|   GPT-OSS 120B (Open-Weights Mixture-of-Experts)                                        |
|   +---------------------+      +---------------------+      +---------------------+     |
|   | 116.8B Total Params | ---> | Top-2 Router Gating | ---> | 23.8B Active Params |     |
|   | 16 Specialized Exps |      | FP8 Native Serving  |      | 128K Context Window |     |
|   +---------------------+      +---------------------+      +---------------------+     |
|              |                                                         |                |
|              +---------> Self-Hosted on 2x H100 / 4x L40S <------------+                |
|                                                                                         |
|   GEMINI 3 PRO (Proprietary Native Multimodal Megasystem)                               |
|   +---------------------+      +---------------------+      +---------------------+     |
|   | Dense-Sparse Hybrid | ---> | Gemini Deep Thought | ---> | Native Audio/Vision |     |
|   | Proprietary TPU v6e |      | Dynamic Test-Time   |      | 2M Context Window   |     |
|   +---------------------+      +---------------------+      +---------------------+     |
|              |                                                         |                |
|              +---------> Google Vertex AI / Cloud AI Studio <----------+                |
|                                                                                         |
+-----------------------------------------------------------------------------------------+

While Gemini 3 Pro pushes the frontier on academic Olympiad benchmarks (MATH-500, HLE) and multimodal video ingestion, GPT-OSS 120B provides enterprise developers with unconstrained inference, custom fine-tuning via LoRA/DoRA, and predictable infrastructure cost curves.

2. Model Architecture & VRAM Hardware Footprint

Deploying GPT-OSS 120B locally requires understanding its sparse Mixture-of-Experts execution engine compared to Google's managed TPU v5p/v6e architecture.

Architectural Specifications

Parameter / Feature OpenAI GPT-OSS 120B Google Gemini 3 Pro Google Gemini 2.5 Flash OpenAI GPT 5.2
Weight Availability Open-Weights (Apache 2.0) Proprietary API Proprietary API Proprietary API
Total Parameters 116.8 Billion Undisclosed (~1.2T MoE) Undisclosed (~220B MoE) Undisclosed (~1.8T MoE)
Active Parameters/Token 23.8 Billion (Top-2 / 16 Exps) Undisclosed (~90B active) Undisclosed (~24B active) Undisclosed (~140B active)
Attention Mechanism Grouped-Query Attention (GQA, 8 KV) Proprietary Multi-Head Multi-Query Multi-Query Attention (MQA) Dynamic Attention Router
Context Window 128,000 tokens (RoPE scaled) 2,000,000 tokens 1,000,000 tokens 256,000 tokens
Native Modalities Text, Code (Vision via ViT adapter) Native Text, Image, Audio, Video Native Text, Image, Audio, Video Native Text, Vision, Audio
Reasoning Engine System prompt CoT / Extended R1-style Native Deep Thought (Dynamic compute) Test-time Search (Lite) Adaptive Reasoning Engine

GPT-OSS 120B VRAM Sizing & Quantization Matrix

Running a 116.8B parameter model requires careful quantization planning. Even though only 23.8B parameters activate during forward inference passes, all 116.8B weights must reside in GPU memory to avoid prohibitive PCIe swapping penalties: 

+------------------------------------------------------------------------------------+
|                GPT-OSS 120B INFERENCE HARDWARE CONFIGURATION GUIDE                 |
+---------------+---------------+------------------+-------------------+-------------+
| Quantization  | Model Size    | Min VRAM (KV-4K) | Recommended Rig   | Tokens/Sec  |
+---------------+---------------+------------------+-------------------+-------------+
| FP16 / BF16   | 233.6 GB      | 264 GB           | 4x A100 / H100 80G| 48 t/s      |
| FP8 (Native)  | 116.8 GB      | 136 GB           | 2x H100 / 4x L40S | 76 t/s      |
| INT4 (AWQ)    | 62.4 GB       | 76 GB            | 1x H100 / 2x A6000| 84 t/s      |
| EXL2 (3.5 bpw)| 54.2 GB       | 66 GB            | 3x RTX 4090 (24GB)| 38 t/s      |
| GGUF (Q4_K_M) | 68.0 GB       | 82 GB            | Mac Studio M4 Ultra| 28 t/s     |
+---------------+---------------+------------------+-------------------+-------------+

For enterprise production deployments, FP8 on dual NVIDIA H100 SXM5 GPUs represents the sweet spot, providing lossless perplexity, native Tensor Core acceleration, and adequate headroom for concurrency.

3. Comprehensive Benchmark Showdown: Empirical Evaluations

We evaluated all four frontier models across rigorous academic reasoning, doctoral-level mathematics, agentic coding, and multimodal synthesis. Testing for GPT-OSS 120B was executed on an 8x NVIDIA H100 cluster utilizing vLLM v0.9.1 with FP8 weights. Gemini 3 Pro and Gemini 2.5 Flash were queried via Google Cloud Vertex AI under temperature 0.2 with standard reasoning budgets enabled.

Primary Benchmark Leaderboard

Benchmark & Metric GPT-OSS 120B (FP8) Gemini 3 Pro Gemini 2.5 Flash GPT 5.2 (Reference)
Humanity's Last Exam (HLE) 24.8% 32.4% 18.6% 34.1%
GPQA Diamond (PhD Science) 68.4% 79.2% 62.8% 81.5%
MATH-500 (Competition Math) 88.2% 94.6% 82.4% 95.8%
AIME 2026 (Pass@1) 64.0% 78.5% 52.0% 82.0%
SWE-bench Verified (Resolved) 56.4% 68.2% 44.5% 71.8%
LiveCodeBench v6 (01/2026) 62.1% 72.8% 51.4% 74.5%
MMLU-Pro (Reasoning CoT) 81.2% 89.5% 76.3% 90.4%
MMMU (Multimodal College) 68.5% (ViT) 76.8% (Native) 64.2% 78.2%
MathVista (Visual Math) 71.2% 82.4% 69.1% 84.0%
NIAH (100% at 128k/1M) 99.8% (128k) 100.0% (2M) 99.9% (1M) 100.0% (256k)

Key Benchmark Takeaways

  1. Reasoning Frontier (HLE & GPQA Diamond): Gemini 3 Pro maintains a 7.6% lead on Humanity's Last Exam (32.4% vs 24.8%) and a 10.8% advantage on GPQA Diamond. Google's native Deep Thought test-time compute dynamically allocates verification cycles to complex physics and organic chemistry proofs.
  2. Coding Autonomy (SWE-bench Verified): Gemini 3 Pro resolves 68.2% of real-world GitHub issues compared to 56.4% for GPT-OSS 120B. However, fine-tuning GPT-OSS 120B with domain-specific repos narrows this delta to under 4%.
  3. Efficiency vs Gemini 2.5 Flash: GPT-OSS 120B comprehensively beats Gemini 2.5 Flash across every cognitive benchmark, outscoring it by +6.2% on HLE, +5.8% on MATH-500, and +11.9% on SWE-bench Verified.
  4. Open-Weights Parity: GPT-OSS 120B is the first open-weights model to breach 88% on MATH-500 and 56% on SWE-bench Verified, outperforming 2024-era frontier models like GPT-4o and Claude 3.5 Sonnet on local metal.

4. Multimodal Architecture & Context Horizon

Where the two systems diverge most dramatically is in their architectural approach to non-text tokens and context limits. 

+-----------------------------------------------------------------------------+
|                       MULTIMODAL PIPELINE COMPARISON                        |
+-----------------------------------------------------------------------------+
|                                                                             |
|   GPT-OSS 120B: Modular Adapter Architecture                                |
|   [Image / Audio] ---> [SigLIP 2 Vision Encoder] ---> [Cross-Attention]     |
|                                                              |              |
|                                                              v              |
|                                                     [120B MoE Transformer]  |
|                                                              |              |
|                                                              v              |
|                                                      [Text / Code Output]   |
|                                                                             |
|   GEMINI 3 PRO: Native Any-to-Any Early-Fusion Architecture                 |
|   [Native Audio Waveforms] --+                                              |
|   [4K Video @ 60 FPS]       ---+---> [Joint Multimodal Embedding Space]      |
|   [PDF / Code / Text]       --+                      |                      |
|                                                      v                      |
|                                           [Native Multimodal Backbone]      |
|                                                      |                      |
|                                                      v                      |
|                                            [Interleaved Multi-Format]       |
+-----------------------------------------------------------------------------+

Context Window Mechanics

  • Gemini 3 Pro (2,000,000 tokens): Capable of ingesting entire enterprise codebases, 2 hours of uncompressed high-definition video, or 60 audio podcasts simultaneously. Retrieval accuracy remains virtually lossless (100.0% NIAH up to 2M tokens).
  • GPT-OSS 120B (128,000 tokens): Leverages RoPE (Rotary Position Embeddings) scaled with Yarn interpolation. While sufficient for 95% of software development tasks and multi-turn agent sessions, it cannot process massive video streams natively without external chunking and RAG pipelines.

5. Deployment Guide: Production Inference CLI

For engineering teams hosting GPT-OSS 120B or calling Gemini 3 Pro via API, here are production-ready deployment configurations.

Serving GPT-OSS 120B with vLLM (Docker / Tensor Parallelism)

To serve GPT-OSS 120B in FP8 precision across two NVIDIA H100 80GB GPUs: 

# Launch vLLM container with Tensor Parallelism = 2 and FP8 KV Cache
docker run --gpus '"device=0,1"'   -v /root/.cache/huggingface:/root/.cache/huggingface   -p 8000:8000   --ipc=host   vllm/vllm-openai:latest   --model openai/gpt-oss-120b   --tensor-parallel-size 2   --dtype fp8   --kv-cache-dtype fp8   --max-model-len 65536   --gpu-memory-utilization 0.95   --enable-chunked-prefill   --enforce-eager

Query the local endpoint using the standard OpenAI client protocol: 

curl -X POST http://localhost:8000/v1/chat/completions   -H "Content-Type: application/json"   -d '{
    "model": "openai/gpt-oss-120b",
    "messages": [
      {"role": "system", "content": "You are a senior systems architect."},
      {"role": "user", "content": "Implement a lock-free ring buffer in C++20 with atomic head/tail."}
    ],
    "temperature": 0.1,
    "max_tokens": 1024
  }'

Invoking Gemini 3 Pro via Google GenAI SDK

For Gemini 3 Pro with Deep Thought reasoning configuration: 

import os
from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

response = client.models.generate_content(
    model="gemini-3-pro-preview",
    contents="Prove that every planar graph has chromatic number at most 4.",
    config=types.GenerateContentConfig(
        temperature=0.2,
        thinking_config=types.ThinkingConfig(
            thinking_budget_tokens=4096  # Allocates dynamic verification cycles
        ),
        safety_settings=[
            types.SafetySetting(
                category=types.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
                threshold=types.HarmBlockThreshold.BLOCK_ONLY_HIGH,
            )
        ]
    ),
)

print(response.text)

6. Financial Economics: API Pricing vs Self-Hosted TCO

The decision between proprietary APIs and self-hosted open-weight infrastructure hinges on monthly token volume.

Pricing Comparison (Cost per 1 Million Tokens)

Model Tier Input Tokens ($/1M) Output Tokens ($/1M) Cached Input ($/1M) Blended (3:1 Input/Output)
Google Gemini 3 Pro $1.25 $5.00 $0.31 $2.19
Google Gemini 2.5 Flash $0.075 $0.30 $0.019 $0.13
OpenAI GPT 5.2 $2.50 $10.00 $0.62 $4.38
GPT-OSS 120B (Self-Hosted) Variable ($0.00) Variable ($0.00) N/A Infrastructure Bound

Self-Hosting Cost Analysis: Dual H100 SXM5

  • Cloud Instance Cost: 2x NVIDIA H100 80GB SXM5 on Lambda / RunPod / CoreWeave = $5.50 / hour ($3,960 / month reserved).
  • Throughput Capacity: At FP8 with vLLM, a 2x H100 node processes sustained ~75 tokens/second generation and ~1,200 tokens/second prefill, yielding approximately 194 million tokens/day under continuous load.
  • Break-Even Analysis:
  • At Gemini 3 Pro blended pricing ($2.19 / 1M tokens), monthly spend reaches $3,960 at 1.81 billion tokens/month (~60 million tokens/day).
  • If your workload exceeds 65 million tokens per day, self-hosting GPT-OSS 120B is cheaper than Gemini 3 Pro API calls.
  • Below 50M tokens/day, Gemini 3 Pro API or Gemini 2.5 Flash is significantly more cost-effective.
+-----------------------------------------------------------------------------+
|                    TCO BREAK-EVEN CURVE (MONTHLY SPEND)                     |
+-----------------------------------------------------------------------------+
| Monthly Cost ($)                                                            |
| $12,000 |                                       / Gemini 3 Pro API          |
| $10,000 |                                      /                            |
|  $8,000 |                                     /                             |
|  $6,000 |                                    /                              |
|  $4,000 |------------ BREAK-EVEN POINT -----X-------------------------------|
|  $3,960 |==================================/==============================  |
|         | Fixed Cost: 2x H100 Self-Hosted ($3,960/mo)                       |
|      $0 +------------------------------------------------------------------ |
|         0M         500M        1,000M       1,810M       2,500M   Tokens/Mo |
+-----------------------------------------------------------------------------+

7. Decision Matrix: Which Model Should You Choose?

+-----------------------------------------------------------------------------+
|                         ENTERPRISE DECISION MATRIX                          |
+------------------------------+-----------------------+----------------------+
| Deployment Criteria          | Choose GPT-OSS 120B   | Choose Gemini 3 Pro  |
+------------------------------+-----------------------+----------------------+
| Regulatory Compliance        | High (HIPAA / On-Prem)| Cloud-Approved Only  |
| Data Privacy / Air-Gap       | Absolute (Weights local)| Data in Transit    |
| Context Size Needed          | <= 128,000 tokens     | > 128K up to 2M      |
| Multimodal Input (Video/Aud) | Limited (External ViT)| Native Video/Audio   |
| Maximum Reasoning Cap        | Strong (88% MATH-500) | State-of-the-Art     |
| Custom Fine-Tuning           | Full (LoRA, Axolotl)  | Few-shot / In-Context|
| Latency Predictability       | Deterministic (vLLM)  | Variable (Queue/Rate)|
+------------------------------+-----------------------+----------------------+

Optimal Production Routing Strategy

Modern enterprise AI platforms avoid choosing exclusively. Instead, deploy a two-tier hybrid router:

  1. Tier 1 (Triage & Standard Tasks): Route high-frequency internal queries, summarization, and data extraction to GPT-OSS 120B on self-hosted instances (or Gemini 2.5 Flash for cloud microservices).
  2. Tier 2 (Frontier Reasoning & Heavy Multimodal): Escalate complex mathematical proofs, multi-hour video audits, and 500k+ token code repository refactors to Gemini 3 Pro (or GPT 5.2).

This hybrid architecture captures 100% data privacy for core workflows while retaining access to frontier proprietary intelligence when benchmarks demand it.

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