Pairwise benchmark snapshot · Aug 16, 2026

Gemini 3.6 Pro vs MoonshotAI: Kimi K2 0711 benchmark

Name: Gemini 3.6 Pro vs MoonshotAI: Kimi K2 0711 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 3.6 Pro (Google) and MoonshotAI: Kimi K2 0711 (Moonshot) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Gemini 3.6 Pro is ahead by 105 points (1,570 vs 1,465; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, Gemini 3.6 Pro resolves 74.6% versus 65.8% (seed-bootstrap (Aug 16, 2026)). Gemini 3.6 Pro streams faster (102 tok/s) while MoonshotAI: Kimi K2 0711 is the cheaper output token ($2.3/1M). Gemini 3.6 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.

Share Analysis:

WhatsApp Telegram X / Post LinkedIn Reddit

Google

Gemini 3.6 Pro

Current Google Pro-class multimodal model. Long context, strong coding, billed like the 3.x Pro tier.

Elo 1,570$5/1M out

Moonshot

MoonshotAI: Kimi K2 0711

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

Elo 1,465$2.3/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

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 3.6 Pro (8)vsMoonshotAI: Kimi K2 0711 (2)

Gemini 3.6 Pro: 8W (80%)Overall: Gemini 3.6 ProMoonshotAI: Kimi K2 0711: 2W (20%)

← Gemini 3.6 ProMoonshotAI: Kimi K2 0711 →

Gemini 3.6 Pro (5/5)

Intelligence & Reasoning

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

Preference Elo

1,570vs1,465

Coding Elo

1,566vs1,470

SWE-bench

74.6%vs65.8%

LiveBench

71.8%vs62.8%

GPQA Diamond

84.4%vs76.5%

Gemini 3.6 Pro: 5WMoonshotAI: Kimi K2 0711: 0W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

102 tok/svs85 tok/s

Time to first token

205 msvs240 ms

Gemini 3.6 Pro: 2WMoonshotAI: Kimi K2 0711: 0W

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 3.6 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 AdvantageGemini 3.6 Pro 5/6

Preference Elo

Gemini 3.6 Pro

105 pts advantage

Throughput Speed

Gemini 3.6 Pro

17 tok/s faster

Price Efficiency

MoonshotAI: Kimi K2 0711

$2.70/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+7%1,570

EloHigh is better

1,465

Code Elo

High is better

+7%1,566

Code EloHigh is better

1,470

LiveBench

High is better

+14%71.8%

LiveBenchHigh is better

62.8%

SWE-bench

High is better

+13%74.6%

SWE-benchHigh is better

65.8%

GPQA

High is better

+10%84.4%

GPQAHigh is better

76.5%

TTFT

Low is better

+17%205 ms

TTFTLow is better

240 ms

Speed

High is better

+20%102 tok/s

SpeedHigh is better

85 tok/s

In $

Low is better

$1.25/1M

In $Low is better

$0.57/1M+119%

Out $

Low is better

$5/1M

Out $Low is better

$2.3/1M+117%

Context

High is better

+1426%2M

ContextHigh is better

131k

Interactive Simulator

Workload Cost & Savings Calculator

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

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 3.6 Pro$118.75 / mo

In: $43.75Out: $75.00

MoonshotAI: Kimi K2 0711$54.45 / mo

In: $19.95Out: $34.50

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
Gemini 3.6 Pro:Pick Gemini 3.6 Pro when repo-level coding accuracy is the constraint.
2
MoonshotAI: Kimi K2 0711:Pick MoonshotAI: Kimi K2 0711 when you are optimizing output cost.
3
Gemini 3.6 Pro:Pick Gemini 3.6 Pro when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsGemini 3.6 Pro

Higher SWE-bench (74.6%).

High-volume chatMoonshotAI: Kimi K2 0711

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

Voice / low-latency UIGemini 3.6 Pro

Lower TTFT (205 ms).

Long-document RAGGemini 3.6 Pro

Larger window (2M).

Screenshots / visionGemini 3.6 Pro

Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Gemini 3.6 Pro	Higher SWE-bench (74.6%).
High-volume chat	MoonshotAI: Kimi K2 0711	Lower output list price ($2.3/1M).
Voice / low-latency UI	Gemini 3.6 Pro	Lower TTFT (205 ms).
Long-document RAG	Gemini 3.6 Pro	Larger window (2M).
Screenshots / vision	Gemini 3.6 Pro	Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).

Related Editorial Dispatches & Benchmark Notes

View all news

compareAug 13, 2026

Gemini Flash vs Pro: when Flash is the whole product

3.7 Flash vs 3.6 Pro is the current Google volume-versus-upgrade pair.

compareJul 24, 2026

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Top Anthropic vs top Google. Usually Opus 5 vs 3.6 Pro. Two different invoices.

compareJul 16, 2026

Gemini vs GPT: long context versus the OpenAI flagship invoice

Hub remaps to top Google vs top OpenAI Elo. Usually 3.6 Pro vs Sol. Window vs price is the plot.

launchJul 15, 2026

Gemini 3.6 Pro: Google’s current Pro-class long-context row

2M context, Pro-class Elo and coding. Upgrade path from Flash is this page, not a vibe check.

Community Sentiment

Cast Your Matchup Vote

0 total votes

Community Discussions (0)

No comments posted on this matchup yet. Be the first to share an evaluation note!

Share card

The real PNG is generated at /compare/gemini-3-6-pro-vs-kimi-k2/opengraph-image for crawlers.

PNG

CompareLLM Matrix

Verified head-to-head card

LIVE

Google

Gemini 3.6 Pro

Elo 1,570

LiveBench71.8%

SWE-bench74.6%

Speed102 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 3.6 Pro or MoonshotAI: Kimi K2 0711?: Gemini 3.6 Pro has the higher preference Elo in our latest snapshot (1,570). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Gemini 3.6 Pro or MoonshotAI: Kimi K2 0711?: Gemini 3.6 Pro leads SWE-bench at 74.6% vs 65.8%. 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 3.6 Pro or MoonshotAI: Kimi K2 0711?: Gemini 3.6 Pro has the lower time-to-first-token (205 ms vs 240 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Gemini 3.6 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 3.6 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 3.6 Pro matchups →|All MoonshotAI: Kimi K2 0711 matchups →

Gemini 3.6 Pro vs Gemini 3 Pro (previous 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 3.7 Flash vs Gemini 3.6 Pro Kimi K3 vs Gemini 3.6 Pro Gemini 3.6 Flash vs MoonshotAI: Kimi K2 0711

Gemini 3.6 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 3.6 Pro (8)vsMoonshotAI: Kimi K2 0711 (2)

Gemini 3.6 Pro: 8W (80%)Overall: Gemini 3.6 ProMoonshotAI: Kimi K2 0711: 2W (20%)

← Gemini 3.6 ProMoonshotAI: Kimi K2 0711 →

Gemini 3.6 Pro (5/5)

Intelligence & Reasoning

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

Preference Elo

1,570vs1,465

Coding Elo

1,566vs1,470

SWE-bench

74.6%vs65.8%

LiveBench

71.8%vs62.8%

GPQA Diamond

84.4%vs76.5%

Gemini 3.6 Pro: 5WMoonshotAI: Kimi K2 0711: 0W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

102 tok/svs85 tok/s

Time to first token

205 msvs240 ms

Gemini 3.6 Pro: 2WMoonshotAI: Kimi K2 0711: 0W

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 3.6 Pro: 1WMoonshotAI: Kimi K2 0711: 2W

Recommended Workload Routing

Repo / coding agentsGemini 3.6 Pro

Higher SWE-bench (74.6%).

High-volume chatMoonshotAI: Kimi K2 0711

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

Voice / low-latency UIGemini 3.6 Pro

Lower TTFT (205 ms).

Long-document RAGGemini 3.6 Pro

Larger window (2M).

Screenshots / visionGemini 3.6 Pro

Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Gemini 3.6 Pro	Higher SWE-bench (74.6%).
High-volume chat	MoonshotAI: Kimi K2 0711	Lower output list price ($2.3/1M).
Voice / low-latency UI	Gemini 3.6 Pro	Lower TTFT (205 ms).
Long-document RAG	Gemini 3.6 Pro	Larger window (2M).
Screenshots / vision	Gemini 3.6 Pro	Both accept images. Defaulting to the higher-Elo side (Gemini 3.6 Pro).

Frequently Asked Questions

Which is better overall, Gemini 3.6 Pro or MoonshotAI: Kimi K2 0711?

Gemini 3.6 Pro has the higher preference Elo in our latest snapshot (1,570). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Gemini 3.6 Pro or MoonshotAI: Kimi K2 0711?

Gemini 3.6 Pro leads SWE-bench at 74.6% vs 65.8%. 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 3.6 Pro or MoonshotAI: Kimi K2 0711?

Gemini 3.6 Pro has the lower time-to-first-token (205 ms vs 240 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 3.6 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 3.6 Pro and MoonshotAI: Kimi K2 0711 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

Gemini 3.6 Pro

Gemini 3.6 Pro

MoonshotAI: Kimi K2 0711

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Gemini 3.6 Pro: Google’s current Pro-class long-context row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Gemini 3.6 Pro

MoonshotAI: Kimi K2 0711

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

Gemini 3.6 Pro

Gemini 3.6 Pro

MoonshotAI: Kimi K2 0711

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Gemini 3.6 Pro: Google’s current Pro-class long-context row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Gemini 3.6 Pro

MoonshotAI: Kimi K2 0711

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons