Pairwise benchmark snapshot · Aug 16, 2026

Gemini 1.5 Pro vs Kimi K3 benchmark

Name: Gemini 1.5 Pro vs Kimi K3 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 1.5 Pro (Google) and Kimi K3 (Moonshot) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Kimi K3 is ahead by 268 points (1,528 vs 1,260; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Kimi K3 resolves 73.8% versus 38% (seed-bootstrap (Aug 1, 2026)). Gemini 1.5 Pro streams faster (70 tok/s) while Gemini 1.5 Pro is the cheaper output token ($5/1M). 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

Moonshot

Kimi K3

Moonshot AI frontier flagship reasoning and agent swarm model with 256k context and top-tier SWE-bench coding capability.

Elo 1,528$15/1M 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 (5)vsKimi K3 (5)

Gemini 1.5 Pro: 5W (50%)Overall: Even matchupKimi K3: 5W (50%)

← Gemini 1.5 ProKimi K3 →

Kimi K3 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,528

Coding Elo

—vs1,535

SWE-bench

38%vs73.8%

LiveBench

49.2%vs69.5%

GPQA Diamond

58%vs88.4%

Gemini 1.5 Pro: 0WKimi K3: 5W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs68 tok/s

Time to first token

240 msvs290 ms

Gemini 1.5 Pro: 2WKimi K3: 0W

Gemini 1.5 Pro (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$15/1M

Input price

$1.25/1Mvs$3/1M

Context window

2Mvs1M

Gemini 1.5 Pro: 3WKimi K3: 0W

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 AdvantageEven 3–3

Preference Elo

Kimi K3

268 pts advantage

Throughput Speed

Gemini 1.5 Pro

2 tok/s faster

Price Efficiency

Gemini 1.5 Pro

$10.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,260

EloHigh is better

1,528+21%

Code Elo

High is better

—

Code EloHigh is better

1,535

LiveBench

High is better

49.2%

LiveBenchHigh is better

69.5%+41%

SWE-bench

High is better

38%

SWE-benchHigh is better

73.8%+94%

GPQA

High is better

58%

GPQAHigh is better

88.4%+52%

TTFT

Low is better

+21%240 ms

TTFTLow is better

290 ms

Speed

High is better

+3%70 tok/s

SpeedHigh is better

68 tok/s

In $

Low is better

+140%$1.25/1M

In $Low is better

$3/1M

Out $

Low is better

+200%$5/1M

Out $Low is better

$15/1M

Context

High is better

+91%2M

ContextHigh is better

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 64% with Gemini 1.5 Pro

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: $75.00

Kimi K3$330.00 / mo

In: $105.00Out: $225.00

Estimated Cost Delta

Gemini 1.5 Pro is estimated to save $211.25/month ($2,535/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Kimi K3:Pick Kimi K3 when repo-level coding accuracy is the constraint.
2
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when you are optimizing output cost.
3
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsKimi K3

Higher SWE-bench (73.8%).

High-volume chatGemini 1.5 Pro

Lower output list price ($5/1M).

Voice / low-latency UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionKimi K3

Both accept images. Defaulting to the higher-Elo side (Kimi K3).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Kimi K3	Higher SWE-bench (73.8%).
High-volume chat	Gemini 1.5 Pro	Lower output list price ($5/1M).
Voice / low-latency UI	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Kimi K3	Both accept images. Defaulting to the higher-Elo side (Kimi K3).

Related Editorial Dispatches & Benchmark Notes

View all news

launchJun 8, 2026

Kimi K3: Moonshot’s long-context open-weight competitive row

Jun 2026. The Kimi vs GPT hub remaps to the top Moonshot Elo vs top OpenAI Elo.

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LIVE

Google

Gemini 1.5 Pro

Elo 1,260

LiveBench49.2%

SWE-bench38%

Speed70 tok/s

Output cost$5/1M

Moonshot

Kimi K3

Elo 1,528

LiveBench69.5%

SWE-bench73.8%

Speed68 tok/s

Output cost$15/1M

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or Kimi K3?: Kimi K3 has the higher preference Elo in our latest snapshot (1,528). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Gemini 1.5 Pro or Kimi K3?: Kimi K3 leads SWE-bench at 73.8% vs 38%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Gemini 1.5 Pro output tokens are $5/1M versus $15/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 1.5 Pro or Kimi K3?: Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Gemini 1.5 Pro accepts 2M tokens versus 1M.
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 Kimi K3 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 Kimi K3 matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)Kimi K3 vs MoonshotAI: Kimi K2.5 (previous kimi)Gemini 3 Pro vs Kimi K3 Gemini 3 Flash vs Kimi K3 Gemini 2.0 Flash Thinking vs Kimi K3 Gemini 3.6 Pro vs Kimi K3

Gemini 1.5 Pro vs Kimi K3 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (5)vsKimi K3 (5)

Gemini 1.5 Pro: 5W (50%)Overall: Even matchupKimi K3: 5W (50%)

← Gemini 1.5 ProKimi K3 →

Kimi K3 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,528

Coding Elo

—vs1,535

SWE-bench

38%vs73.8%

LiveBench

49.2%vs69.5%

GPQA Diamond

58%vs88.4%

Gemini 1.5 Pro: 0WKimi K3: 5W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs68 tok/s

Time to first token

240 msvs290 ms

Gemini 1.5 Pro: 2WKimi K3: 0W

Gemini 1.5 Pro (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$15/1M

Input price

$1.25/1Mvs$3/1M

Context window

2Mvs1M

Gemini 1.5 Pro: 3WKimi K3: 0W

Recommended Workload Routing

Repo / coding agentsKimi K3

Higher SWE-bench (73.8%).

High-volume chatGemini 1.5 Pro

Lower output list price ($5/1M).

Voice / low-latency UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionKimi K3

Both accept images. Defaulting to the higher-Elo side (Kimi K3).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Kimi K3	Higher SWE-bench (73.8%).
High-volume chat	Gemini 1.5 Pro	Lower output list price ($5/1M).
Voice / low-latency UI	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Kimi K3	Both accept images. Defaulting to the higher-Elo side (Kimi K3).

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or Kimi K3?

Kimi K3 has the higher preference Elo in our latest snapshot (1,528). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Gemini 1.5 Pro or Kimi K3?

Kimi K3 leads SWE-bench at 73.8% vs 38%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Gemini 1.5 Pro output tokens are $5/1M versus $15/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 1.5 Pro or Kimi K3?

Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 1.5 Pro accepts 2M tokens versus 1M.

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 Kimi K3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

Kimi K3

Gemini 1.5 Pro

Gemini 1.5 Pro

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Kimi K3: Moonshot’s long-context open-weight competitive row

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Gemini 1.5 Pro

Kimi K3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

Kimi K3

Gemini 1.5 Pro

Gemini 1.5 Pro

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Kimi K3: Moonshot’s long-context open-weight competitive row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Gemini 1.5 Pro

Kimi K3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons