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  3. Google: Gemini 3.1 Pro Preview (batch) vs OpenAI o1-mini

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

Google: Gemini 3.1 Pro Preview (batch) vs OpenAI o1-mini benchmark

In this head-to-head showdown, OpenAI o1-mini is more budget-friendly at $4.4/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.

Executive Comparison Verdict

Bottom Line: Choose Google: Gemini 3.1 Pro Preview (batch) if your priority is peak reasoning, complex code generation, and top preference Elo. Choose OpenAI o1-mini if you are optimizing for low latency, high throughput, and cost-efficient API deployment.
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Executive Verdict Summary

Google: Gemini 3.1 Pro Preview (batch) holds empirical advantage (2 of 3 metrics)

Coding / SWESWE-bench
OpenAI o1-miniTop code solve rate
ReasoningGPQA / Live
OpenAI o1-miniTop logical accuracy
Best Value$/1M Tok
OpenAI o1-miniLowest API billing cost
Speed / TokTok/Sec
OpenAI o1-miniFastest stream rate

Verified Advantage Breakdown

3 benchmarks evaluated across capability, speed, and cost

Google: Gemini 3.1 Pro Preview (batch) (2)OpenAI o1-mini (1)
Google: Gemini 3.1 Pro Preview (batch)
2 of 3 Wins
✓Input price✓Context window
OpenAI o1-mini
1 of 3 Wins
✓Output price
Google
Leads 2 of 3
Google: Gemini 3.1 Pro Preview (batch)

Auto-discovered from OpenRouter (google/gemini-3.1-pro-preview:batch). Preview until a second source matches.

In: $1 · Out: $6/1M
OpenAI
Leads 1 of 3
OpenAI o1-mini

OpenAI high-speed, cost-effective reasoning model optimized for STEM, math, and code generation.

Elo 1,445In: $1.1 · Out: $4.4/1M
Top Rival Showdowns for Google: Gemini 3.1 Pro Preview (batch)
All Matchups
Compare vs:vs Claude Opus 4.5vs GPT-5vs Grok 4.6vs DeepSeek V4 Provs Qwen 3 Max
Preference Leader

No shared data

Single model data

Throughput Leader

No shared data

No latency data

Value per Dollar LeaderOpenAI o1-mini Wins

OpenAI o1-mini

$4.4 / 1M output

Capability Percentiles

Relative percentile scores computed across all active models in the benchmark catalog.

Multi-Dimensional Capability Radar

Leaders Matchup Capability Radar

Comparing top standard models across 6 key capability dimensions. Tap any spoke or dot to inspect.

Tap any node to inspect
0–100 %ile

Overall Matchup Breakdown

Category wins across reasoning intelligence, generation speed, and token cost.

Google: Gemini 3.1 Pro Preview (batch) (2)vsOpenAI o1-mini (8)
Google: Gemini 3.1 Pro Preview (batch): 2W (20%)Overall: OpenAI o1-miniOpenAI o1-mini: 8W (80%)
← Google: Gemini 3.1 Pro Preview (batch)OpenAI o1-mini →
🏆OpenAI o1-mini Leads(5/5)

Intelligence & Reasoning

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

Benchmark
Google: Gemini 3.1 Pro Preview (batch)vsOpenAI o1-mini
Preference Elo
—vs1,445
Coding Elo
—vs1,468
SWE-bench
—vs56.4%
LiveBench
—vs62%
GPQA Diamond
—vs76.8%
Google: Gemini 3.1 Pro Preview (batch): 0WOpenAI o1-mini: 5W
🏆OpenAI o1-mini Leads(2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Benchmark
Google: Gemini 3.1 Pro Preview (batch)vsOpenAI o1-mini
Output speed
—vs85 tok/s
Time to first token
—vs280 ms
Google: Gemini 3.1 Pro Preview (batch): 0WOpenAI o1-mini: 2W
🏆Google: Gemini 3.1 Pro Preview (batch) Leads(2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Benchmark
Google: Gemini 3.1 Pro Preview (batch)vsOpenAI o1-mini
Output price
$6/1Mvs$4.4/1M+$1.6/1M
Input price
$1/1Mvs$1.1/1M+$0.1/1M
Context window
1Mvs128k+921k
Google: Gemini 3.1 Pro Preview (batch): 2WOpenAI o1-mini: 1W

Side-by-Side Benchmark Matrix

Google: Gemini 3.1 Pro Preview (batch)OpenAI o1-mini
Preference Elo
Google: Gemini 3.1 Pro Preview (batch)—
OpenAI o1-mini1,445
Coding Elo
Google: Gemini 3.1 Pro Preview (batch)—
OpenAI o1-mini1,468
LiveBench
Google: Gemini 3.1 Pro Preview (batch)—
OpenAI o1-mini62%
SWE-bench
Google: Gemini 3.1 Pro Preview (batch)—
OpenAI o1-mini56.4%
GPQA Diamond
Google: Gemini 3.1 Pro Preview (batch)—
OpenAI o1-mini76.8%
Time to first token
Google: Gemini 3.1 Pro Preview (batch)—
OpenAI o1-mini280 ms
Output speed
Google: Gemini 3.1 Pro Preview (batch)—
OpenAI o1-mini85 tok/s
Input priceGoogle: Gemini 3.1 Pro Preview (batch) +$0.1/1M
Google: Gemini 3.1 Pro Preview (batch)$1/1M
OpenAI o1-mini$1.1/1M
Output priceOpenAI o1-mini +$1.6/1M
Google: Gemini 3.1 Pro Preview (batch)$6/1M
OpenAI o1-mini$4.4/1M
Context windowGoogle: Gemini 3.1 Pro Preview (batch) +921k
Google: Gemini 3.1 Pro Preview (batch)1M
OpenAI o1-mini128k
BenchmarkGoogle: Gemini 3.1 Pro Preview (batch)OpenAI o1-miniAdvantage Delta
Preference Elo—
1,445
lmarena · Aug 17, 2026
—
Coding Elo—
1,468
lmarena · Aug 17, 2026
—
LiveBench—
62%
livebench · Aug 17, 2026
—
SWE-bench—
56.4%
swebench · Aug 17, 2026
—
GPQA Diamond—
76.8%
seed-bootstrap · Aug 1, 2026
—
Time to first token—
280 ms
seed-bootstrap · Aug 1, 2026
—
Output speed—
85 tok/s
seed-bootstrap · Aug 1, 2026
—
Input price
$1/1M
openrouter · Aug 17, 2026
$1.1/1M
seed-bootstrap · Aug 1, 2026
+$0.1/1M
Output price
$6/1M
openrouter · Aug 17, 2026
$4.4/1M
seed-bootstrap · Aug 1, 2026
+$1.6/1M
Context window
1M
openrouter · Aug 17, 2026
128k
seed-bootstrap · Aug 1, 2026
+921k

Workload Cost & Savings Calculator

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

Save 16% with OpenAI o1-mini
Monthly Volume50M tokens/mo
Quick Presets:
Token Ratio (In vs Out)70% In / 30% Out
RAG / Search (10% out)Coding / Chat (50% out)
Google: Gemini 3.1 Pro Preview (batch)$125.00 / mo
In: $35Out: $90
OpenAI o1-mini$104.50 / mo
In: $38.5Out: $66
Estimated Cost Delta

OpenAI o1-mini is estimated to save $20.50/month ($246/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

  • 1
    OpenAI o1-mini:Pick OpenAI o1-mini when you are optimizing output cost.
  • 2
    Google: Gemini 3.1 Pro Preview (batch):Pick Google: Gemini 3.1 Pro Preview (batch) for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI o1-mini

Lower output list price ($4.4/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGoogle: Gemini 3.1 Pro Preview (batch)

Larger window (1M).

Screenshots / visionOpenAI o1-mini

OpenAI o1-mini is the side marked multimodal in the catalog.

Target WorkloadRecommended PickEvaluation Rationale
Repo / coding agentsinsufficient dataNeed SWE-bench on both sides.
High-volume chatOpenAI o1-miniLower output list price ($4.4/1M).
Voice / low-latency UIinsufficient dataNeed TTFT on both sides.
Long-document RAGGoogle: Gemini 3.1 Pro Preview (batch)Larger window (1M).
Screenshots / visionOpenAI o1-miniOpenAI o1-mini is the side marked multimodal in the catalog.

Cast Your Matchup Vote

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CompareLLM AI Matrix

Verified Head-to-Head Telemetry

LIVE BENCHMARK
VS
Google

Google: Gemini 3.1 Pro Preview (batch)

Elo n/a
SWE-bench—
Speed (tok/s)1M
Input / 1M Tokens$1/1M
Output / 1M Tokens$6/1M
OpenAI

OpenAI o1-mini

Elo 1,445
SWE-bench56.4%
Speed (tok/s)85 tok/s
Input / 1M Tokens$1.1/1M
Output / 1M Tokens$4.4/1M

Frequently Asked Questions

Which is better overall, Google: Gemini 3.1 Pro Preview (batch) or OpenAI o1-mini?
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, Google: Gemini 3.1 Pro Preview (batch) or OpenAI o1-mini?
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 o1-mini output tokens are $4.4/1M versus $6/1M. Input prices and retry rates still move the real bill.
Which is faster, Google: Gemini 3.1 Pro Preview (batch) or OpenAI o1-mini?
We do not have TTFT for both models.
Which has the larger context window?
Google: Gemini 3.1 Pro Preview (batch) accepts 1M tokens versus 128k.
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. Google: Gemini 3.1 Pro Preview (batch) and OpenAI o1-mini Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Google: Gemini 3.1 Pro Preview (batch) matchups →|All OpenAI o1-mini matchups →
OpenAI o1-mini vs GPT-4o mini (previous gpt-mini)OpenAI o1-mini vs OpenAI: o3 Mini (next gpt-mini)GPT-5 vs OpenAI o1-miniGemini 3 Pro vs OpenAI o1-miniGemini 3 Flash vs OpenAI o1-miniGPT-4.5 Orion vs OpenAI o1-mini