Pairwise benchmark snapshot · Aug 17, 2026

Gemini 1.5 Pro vs MoonshotAI: Kimi K2 0711 benchmark

Name: Gemini 1.5 Pro vs MoonshotAI: Kimi K2 0711 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 1.5 Pro (Google) and MoonshotAI: Kimi K2 0711 (Moonshot) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, MoonshotAI: Kimi K2 0711 is ahead by 205 points (1,465 vs 1,260; lmarena (Aug 17, 2026)). On SWE-bench coding, MoonshotAI: Kimi K2 0711 resolves 65.8% versus 38% (swebench (Aug 17, 2026)). MoonshotAI: Kimi K2 0711 streams faster (85 tok/s) while MoonshotAI: Kimi K2 0711 is the cheaper output token ($2.3/1M). 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

MoonshotAI: Kimi K2 0711

Auto-discovered from OpenRouter (moonshotai/kimi-k2). Preview until a second source matches.

K2 BaseElo 1,465$2.3/1M out

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (1)vsMoonshotAI: Kimi K2 0711 (8)

Gemini 1.5 Pro: 1W (11%)Overall: MoonshotAI: Kimi K2 0711MoonshotAI: Kimi K2 0711: 8W (89%)

← Gemini 1.5 Pro1 tiesMoonshotAI: Kimi K2 0711 →

MoonshotAI: Kimi K2 0711 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,465

Coding Elo

—vs1,470

SWE-bench

38%vs65.8%

LiveBench

49.2%vs62.8%

GPQA Diamond

58%vs76.5%

Gemini 1.5 Pro: 0WMoonshotAI: Kimi K2 0711: 5W

MoonshotAI: Kimi K2 0711 (1/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs85 tok/s

Time to first token

240 msvs240 ms

Gemini 1.5 Pro: 0WMoonshotAI: Kimi K2 0711: 1W

MoonshotAI: Kimi K2 0711 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$2.3/1M

Input price

$1.25/1Mvs$0.57/1M

Context window

2Mvs131k

Gemini 1.5 Pro: 1WMoonshotAI: Kimi K2 0711: 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 AdvantageMoonshotAI: Kimi K2 0711 5/6

Preference Elo

MoonshotAI: Kimi K2 0711

205 pts advantage

Throughput Speed

MoonshotAI: Kimi K2 0711

15 tok/s faster

Token Price Efficiency

MoonshotAI: Kimi K2 0711

$2.70/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,260

EloHigh is better

1,465+16%

Code Elo

High is better

—

Code EloHigh is better

1,470

LiveBench

High is better

49.2%

LiveBenchHigh is better

62.8%+28%

SWE-bench

High is better

38%

SWE-benchHigh is better

65.8%+73%

GPQA

High is better

58%

GPQAHigh is better

76.5%+32%

TTFT

Low is better

240 ms

TTFTLow is better

240 ms

Speed

High is better

70 tok/s

SpeedHigh is better

85 tok/s+21%

In $

Low is better

$1.25 / 1M

In $Low is better

$0.57 / 1M+119%

Out $

Low is better

$5.00 / 1M

Out $Low is better

$2.30 / 1M+117%

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 54% with MoonshotAI: Kimi K2 0711

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

MoonshotAI: Kimi K2 0711$54.45 / mo

In: $19.95Out: $34.5

Estimated Cost Delta

MoonshotAI: Kimi K2 0711 is estimated to save $64.30/month ($772/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
MoonshotAI: Kimi K2 0711:Pick MoonshotAI: Kimi K2 0711 when repo-level coding accuracy is the constraint.
2
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when time-to-first-token matters more than peak Elo.
3
Gemini 1.5 Pro:Pick Gemini 1.5 Pro for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsMoonshotAI: Kimi K2 0711

Higher SWE-bench (65.8%).

High-volume chatMoonshotAI: Kimi K2 0711

Lower output list price ($2.3/1M).

Voice / low-latency UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionMoonshotAI: Kimi K2 0711

Both accept images. Defaulting to the higher-Elo side (MoonshotAI: Kimi K2 0711).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	MoonshotAI: Kimi K2 0711	Higher SWE-bench (65.8%).
High-volume chat	MoonshotAI: Kimi K2 0711	Lower output list price ($2.3/1M).
Voice / low-latency UI	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	MoonshotAI: Kimi K2 0711	Both accept images. Defaulting to the higher-Elo side (MoonshotAI: Kimi K2 0711).

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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

MoonshotAI: Kimi K2 0711

Elo 1,465

LiveBench62.8%

SWE-bench65.8%

Speed85 tok/s

Output cost$2.3/1M

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or MoonshotAI: Kimi K2 0711?: MoonshotAI: Kimi K2 0711 has the higher preference Elo in our latest snapshot (1,465). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Gemini 1.5 Pro or MoonshotAI: Kimi K2 0711?: MoonshotAI: Kimi K2 0711 leads SWE-bench at 65.8% vs 38%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: MoonshotAI: Kimi K2 0711 output tokens are $2.3/1M versus $5/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 1.5 Pro or MoonshotAI: Kimi K2 0711?: Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 240 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 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 MoonshotAI: Kimi K2 0711 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 MoonshotAI: Kimi K2 0711 matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)MoonshotAI: Kimi K2 0711 vs Kimi Chat 1.5 (previous kimi)MoonshotAI: Kimi K2 0711 vs MoonshotAI: Kimi K2.5 (next kimi)Gemini 3 Pro vs MoonshotAI: Kimi K2 0711 Gemini 3 Flash vs MoonshotAI: Kimi K2 0711 Kimi K3 vs MoonshotAI: Kimi K2 0711

Gemini 1.5 Pro vs MoonshotAI: Kimi K2 0711 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (1)vsMoonshotAI: Kimi K2 0711 (8)

Gemini 1.5 Pro: 1W (11%)Overall: MoonshotAI: Kimi K2 0711MoonshotAI: Kimi K2 0711: 8W (89%)

← Gemini 1.5 Pro1 tiesMoonshotAI: Kimi K2 0711 →

MoonshotAI: Kimi K2 0711 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,465

Coding Elo

—vs1,470

SWE-bench

38%vs65.8%

LiveBench

49.2%vs62.8%

GPQA Diamond

58%vs76.5%

Gemini 1.5 Pro: 0WMoonshotAI: Kimi K2 0711: 5W

MoonshotAI: Kimi K2 0711 (1/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs85 tok/s

Time to first token

240 msvs240 ms

Gemini 1.5 Pro: 0WMoonshotAI: Kimi K2 0711: 1W

MoonshotAI: Kimi K2 0711 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$2.3/1M

Input price

$1.25/1Mvs$0.57/1M

Context window

2Mvs131k

Gemini 1.5 Pro: 1WMoonshotAI: Kimi K2 0711: 2W

Recommended Workload Routing

Repo / coding agentsMoonshotAI: Kimi K2 0711

Higher SWE-bench (65.8%).

High-volume chatMoonshotAI: Kimi K2 0711

Lower output list price ($2.3/1M).

Voice / low-latency UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionMoonshotAI: Kimi K2 0711

Both accept images. Defaulting to the higher-Elo side (MoonshotAI: Kimi K2 0711).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	MoonshotAI: Kimi K2 0711	Higher SWE-bench (65.8%).
High-volume chat	MoonshotAI: Kimi K2 0711	Lower output list price ($2.3/1M).
Voice / low-latency UI	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	MoonshotAI: Kimi K2 0711	Both accept images. Defaulting to the higher-Elo side (MoonshotAI: Kimi K2 0711).

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or MoonshotAI: Kimi K2 0711?

MoonshotAI: Kimi K2 0711 has the higher preference Elo in our latest snapshot (1,465). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Gemini 1.5 Pro or MoonshotAI: Kimi K2 0711?

MoonshotAI: Kimi K2 0711 leads SWE-bench at 65.8% vs 38%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

MoonshotAI: Kimi K2 0711 output tokens are $2.3/1M versus $5/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 1.5 Pro or MoonshotAI: Kimi K2 0711?

Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 240 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 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 MoonshotAI: Kimi K2 0711 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.