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

Google: Gemma 4 26B A4B vs MoonshotAI: Kimi K2.5 benchmark

Name: Google: Gemma 4 26B A4B vs MoonshotAI: Kimi K2.5 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Google: Gemma 4 26B A4B (Google) and MoonshotAI: Kimi K2.5 (Moonshot) using the latest snapshots we have as of Aug 17, 2026. Google: Gemma 4 26B A4B wins on output price at $0.34/1M. Google: Gemma 4 26B A4B has the larger context window (262k 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

Google: Gemma 4 26B A4B

Auto-discovered from OpenRouter (google/gemma-4-26b-a4b-it). Preview until a second source matches.

$0.33999999999999997/1M out

Moonshot

MoonshotAI: Kimi K2.5

Auto-discovered from OpenRouter (moonshotai/kimi-k2.5). Preview until a second source matches.

K2.5 MaxElo 1,515$2.8499999999999996/1M out

MoonshotAI: Kimi K2.5 Inference Compute Profile

Inference-Time Compute & Reasoning EffortDynamic CoT

MoonshotAI: Kimi K2.5 Reasoning Scaler & Token Billing Simulator

Param: thinking_mode

Reasoning models scale test-time compute by generating hidden chain-of-thought (CoT) tokens. These tokens are billed at standard output rates and directly expand latency (TTFT) in exchange for higher GPQA Diamond and SWE-bench Verified problem resolve rates.

Estimated TTFT

270 ms

1.0x (Standard eval)

CoT Thinking Tokens

~2,500

Billed as output tokens

Est. Cost / 1k Calls

$8.27

7.3x base invoice

STEM / SWE Boost

+5.5%

GPQA & SWE-bench scaling

Medium (Deep Reasoning) Operational Profile

Kimi Swarm reasoning trace for long-context planning.

Direct Provider APIMoonshot

"thinking_mode": "medium"

OpenRouter Unified APIUnified Gateway

"reasoning": {
  "effort": "medium",
  "max_tokens": 2500
}

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.

Google: Gemma 4 26B A4B (2)vsMoonshotAI: Kimi K2.5 (7)

Google: Gemma 4 26B A4B : 2W (22%)Overall: MoonshotAI: Kimi K2.5MoonshotAI: Kimi K2.5: 7W (78%)

← Google: Gemma 4 26B A4B 1 tiesMoonshotAI: Kimi K2.5 →

MoonshotAI: Kimi K2.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,515

Coding Elo

—vs1,520

SWE-bench

—vs71.3%

LiveBench

—vs67.2%

GPQA Diamond

—vs87.6%

Google: Gemma 4 26B A4B : 0WMoonshotAI: Kimi K2.5: 5W

MoonshotAI: Kimi K2.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs72 tok/s

Time to first token

—vs270 ms

Google: Gemma 4 26B A4B : 0WMoonshotAI: Kimi K2.5: 2W

Google: Gemma 4 26B A4B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.34/1Mvs$2.85/1M

Input price

$0.07/1Mvs$0.57/1M

Context window

262kvs262k

Google: Gemma 4 26B A4B : 2WMoonshotAI: Kimi K2.5: 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 AdvantageMoonshotAI: Kimi K2.5 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

Google: Gemma 4 26B A4B

$2.51/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,515

Code Elo

High is better

—

Code EloHigh is better

1,520

LiveBench

High is better

—

LiveBenchHigh is better

67.2%

SWE-bench

High is better

—

SWE-benchHigh is better

71.3%

GPQA

High is better

—

GPQAHigh is better

87.6%

TTFT

Low is better

—

TTFTLow is better

270 ms

Speed

High is better

—

SpeedHigh is better

72 tok/s

In $

Low is better

+714%$0.07 / 1M

In $Low is better

$0.57 / 1M

Out $

Low is better

+738%$0.34 / 1M

Out $Low is better

$2.85 / 1M

Context

High is better

262k

ContextHigh is better

262k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 88% with Google: Gemma 4 26B A4B

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Google: Gemma 4 26B A4B $7.55 / mo

In: $2.45Out: $5.1

MoonshotAI: Kimi K2.5$62.70 / mo

In: $19.95Out: $42.75

Estimated Cost Delta

Google: Gemma 4 26B A4B is estimated to save $55.15/month ($662/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Google: Gemma 4 26B A4B :Pick Google: Gemma 4 26B A4B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatGoogle: Gemma 4 26B A4B

Lower output list price ($0.34/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGoogle: Gemma 4 26B A4B

Larger window (262k).

Screenshots / visionMoonshotAI: Kimi K2.5

MoonshotAI: Kimi K2.5 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	Google: Gemma 4 26B A4B	Lower output list price ($0.34/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Google: Gemma 4 26B A4B	Larger window (262k).
Screenshots / vision	MoonshotAI: Kimi K2.5	MoonshotAI: Kimi K2.5 is the side marked multimodal in the catalog.

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CompareLLM Matrix

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LIVE

Google

Google: Gemma 4 26B A4B

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.34/1M

Moonshot

MoonshotAI: Kimi K2.5

Elo 1,515

LiveBench67.2%

SWE-bench71.3%

Speed72 tok/s

Output cost$2.85/1M

Frequently Asked Questions

Which is better overall, Google: Gemma 4 26B A4B or MoonshotAI: Kimi K2.5?: 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: Gemma 4 26B A4B or MoonshotAI: Kimi K2.5?: 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?: Google: Gemma 4 26B A4B output tokens are $0.34/1M versus $2.85/1M. Input prices and retry rates still move the real bill.
Which is faster, Google: Gemma 4 26B A4B or MoonshotAI: Kimi K2.5?: We do not have TTFT for both models.
Which has the larger context window?: Google: Gemma 4 26B A4B accepts 262k tokens versus 262k.
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: Gemma 4 26B A4B and MoonshotAI: Kimi K2.5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Google: Gemma 4 26B A4B matchups →|All MoonshotAI: Kimi K2.5 matchups →

MoonshotAI: Kimi K2.5 vs MoonshotAI: Kimi K2 0711 (previous kimi)MoonshotAI: Kimi K2.5 vs Kimi K3 (next kimi)Gemini 3 Pro vs MoonshotAI: Kimi K2.5 Gemini 3 Flash vs MoonshotAI: Kimi K2.5 Gemini 2.0 Flash Thinking vs MoonshotAI: Kimi K2.5 Gemini 3.6 Pro vs MoonshotAI: Kimi K2.5

Google: Gemma 4 26B A4B vs MoonshotAI: Kimi K2.5 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Google: Gemma 4 26B A4B (2)vsMoonshotAI: Kimi K2.5 (7)

Google: Gemma 4 26B A4B : 2W (22%)Overall: MoonshotAI: Kimi K2.5MoonshotAI: Kimi K2.5: 7W (78%)

← Google: Gemma 4 26B A4B 1 tiesMoonshotAI: Kimi K2.5 →

MoonshotAI: Kimi K2.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,515

Coding Elo

—vs1,520

SWE-bench

—vs71.3%

LiveBench

—vs67.2%

GPQA Diamond

—vs87.6%

Google: Gemma 4 26B A4B : 0WMoonshotAI: Kimi K2.5: 5W

MoonshotAI: Kimi K2.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs72 tok/s

Time to first token

—vs270 ms

Google: Gemma 4 26B A4B : 0WMoonshotAI: Kimi K2.5: 2W

Google: Gemma 4 26B A4B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.34/1Mvs$2.85/1M

Input price

$0.07/1Mvs$0.57/1M

Context window

262kvs262k

Google: Gemma 4 26B A4B : 2WMoonshotAI: Kimi K2.5: 0W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatGoogle: Gemma 4 26B A4B

Lower output list price ($0.34/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGoogle: Gemma 4 26B A4B

Larger window (262k).

Screenshots / visionMoonshotAI: Kimi K2.5

MoonshotAI: Kimi K2.5 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	Google: Gemma 4 26B A4B	Lower output list price ($0.34/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Google: Gemma 4 26B A4B	Larger window (262k).
Screenshots / vision	MoonshotAI: Kimi K2.5	MoonshotAI: Kimi K2.5 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Google: Gemma 4 26B A4B or MoonshotAI: Kimi K2.5?

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: Gemma 4 26B A4B or MoonshotAI: Kimi K2.5?

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?

Google: Gemma 4 26B A4B output tokens are $0.34/1M versus $2.85/1M. Input prices and retry rates still move the real bill.

Which is faster, Google: Gemma 4 26B A4B or MoonshotAI: Kimi K2.5?

We do not have TTFT for both models.

Which has the larger context window?

Google: Gemma 4 26B A4B accepts 262k tokens versus 262k.

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: Gemma 4 26B A4B and MoonshotAI: Kimi K2.5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.