Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

Gemini 1.5 Pro vs OpenAI: gpt-oss-120b benchmark

Name: Gemini 1.5 Pro vs OpenAI: gpt-oss-120b AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 1.5 Pro (Google) and OpenAI: gpt-oss-120b (OpenAI) using the latest snapshots we have as of Aug 17, 2026. OpenAI: gpt-oss-120b wins on output price at $0.17/1M. Gemini 1.5 Pro has the larger context window (2M tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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Google

Gemini 1.5 Pro

First million-token Gemini Pro. Baseline for 1.5 vs 2.5 vs 3.x Pro.

Elo 1,260$5/1M out

OpenAI

OpenAI: gpt-oss-120b

Auto-discovered from OpenRouter (openai/gpt-oss-120b). Preview until a second source matches.

$0.16999999999999998/1M out

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Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (7)vsOpenAI: gpt-oss-120b (2)

Gemini 1.5 Pro: 7W (78%)Overall: Gemini 1.5 ProOpenAI: gpt-oss-120b: 2W (22%)

← Gemini 1.5 ProOpenAI: gpt-oss-120b →

Gemini 1.5 Pro (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,260vs—

Coding Elo

—vs—

SWE-bench

38%vs—

LiveBench

49.2%vs—

GPQA Diamond

58%vs—

Gemini 1.5 Pro: 4WOpenAI: gpt-oss-120b: 0W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs—

Time to first token

240 msvs—

Gemini 1.5 Pro: 2WOpenAI: gpt-oss-120b: 0W

OpenAI: gpt-oss-120b (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$0.17/1M

Input price

$1.25/1Mvs$0.03/1M

Context window

2Mvs131k

Gemini 1.5 Pro: 1WOpenAI: gpt-oss-120b: 2W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageGemini 1.5 Pro 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

OpenAI: gpt-oss-120b

$4.83/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,260

EloHigh is better

—

LiveBench

High is better

49.2%

LiveBenchHigh is better

—

SWE-bench

High is better

38%

SWE-benchHigh is better

—

GPQA

High is better

58%

GPQAHigh is better

—

TTFT

Low is better

240 ms

TTFTLow is better

—

Speed

High is better

70 tok/s

SpeedHigh is better

—

In $

Low is better

$1.25 / 1M

In $Low is better

$0.03 / 1M+4067%

Out $

Low is better

$5.00 / 1M

Out $Low is better

$0.17 / 1M+2841%

Context

High is better

+1426%2M

ContextHigh is better

131k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 97% with OpenAI: gpt-oss-120b

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

Gemini 1.5 Pro$118.75 / mo

In: $43.75Out: $75

OpenAI: gpt-oss-120b$3.60 / mo

In: $1.05Out: $2.55

Estimated Cost Delta

OpenAI: gpt-oss-120b is estimated to save $115.15/month ($1,382/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: gpt-oss-120b:Pick OpenAI: gpt-oss-120b when you are optimizing output cost.
2
Gemini 1.5 Pro:Pick Gemini 1.5 Pro for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: gpt-oss-120b

Lower output list price ($0.17/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	OpenAI: gpt-oss-120b	Lower output list price ($0.17/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

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LIVE

Google

Gemini 1.5 Pro

Elo 1,260

LiveBench49.2%

SWE-bench38%

Speed70 tok/s

Output cost$5/1M

OpenAI

OpenAI: gpt-oss-120b

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.17/1M

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or OpenAI: gpt-oss-120b?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, Gemini 1.5 Pro or OpenAI: gpt-oss-120b?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: OpenAI: gpt-oss-120b output tokens are $0.17/1M versus $5/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 1.5 Pro or OpenAI: gpt-oss-120b?: We do not have TTFT for both models.
Which has the larger context window?: Gemini 1.5 Pro accepts 2M tokens versus 131k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Gemini 1.5 Pro and OpenAI: gpt-oss-120b Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Gemini 1.5 Pro matchups →|All OpenAI: gpt-oss-120b matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)GPT-5 vs Gemini 1.5 Pro Gemini 3 Pro vs Gemini 1.5 Pro Gemini 3 Flash vs Gemini 1.5 Pro GPT-4.5 Orion vs Gemini 1.5 Pro Gemini 2.0 Flash Thinking vs Gemini 1.5 Pro

Gemini 1.5 Pro vs OpenAI: gpt-oss-120b benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (7)vsOpenAI: gpt-oss-120b (2)

Gemini 1.5 Pro: 7W (78%)Overall: Gemini 1.5 ProOpenAI: gpt-oss-120b: 2W (22%)

← Gemini 1.5 ProOpenAI: gpt-oss-120b →

Gemini 1.5 Pro (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,260vs—

Coding Elo

—vs—

SWE-bench

38%vs—

LiveBench

49.2%vs—

GPQA Diamond

58%vs—

Gemini 1.5 Pro: 4WOpenAI: gpt-oss-120b: 0W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs—

Time to first token

240 msvs—

Gemini 1.5 Pro: 2WOpenAI: gpt-oss-120b: 0W

OpenAI: gpt-oss-120b (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$0.17/1M

Input price

$1.25/1Mvs$0.03/1M

Context window

2Mvs131k

Gemini 1.5 Pro: 1WOpenAI: gpt-oss-120b: 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: gpt-oss-120b

Lower output list price ($0.17/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	OpenAI: gpt-oss-120b	Lower output list price ($0.17/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or OpenAI: gpt-oss-120b?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, Gemini 1.5 Pro or OpenAI: gpt-oss-120b?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

OpenAI: gpt-oss-120b output tokens are $0.17/1M versus $5/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 1.5 Pro or OpenAI: gpt-oss-120b?

We do not have TTFT for both models.

Which has the larger context window?

Gemini 1.5 Pro accepts 2M tokens versus 131k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Gemini 1.5 Pro and OpenAI: gpt-oss-120b Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.