Pairwise benchmark snapshot · Aug 17, 2026

MoonshotAI: Kimi K2.5 vs Qwen2.5 Coder 32B Instruct benchmark

Name: MoonshotAI: Kimi K2.5 vs Qwen2.5 Coder 32B Instruct AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares MoonshotAI: Kimi K2.5 (Moonshot) and Qwen2.5 Coder 32B Instruct (Alibaba) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, MoonshotAI: Kimi K2.5 is ahead by 90 points (1,515 vs 1,425; lmarena (Aug 17, 2026)). On SWE-bench coding, MoonshotAI: Kimi K2.5 resolves 71.3% versus 65.2% (swebench (Aug 17, 2026)). Qwen2.5 Coder 32B Instruct wins on output price at $1/1M. MoonshotAI: Kimi K2.5 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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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

Alibaba

Qwen2.5 Coder 32B Instruct

Auto-discovered from OpenRouter (qwen/qwen-2.5-coder-32b-instruct). Preview until a second source matches.

Elo 1,425$1/1M out

Model Lineage & Multi-Tier Derivatives🇨🇳 CN Frontier

Kimi Series Family (4 models)

Compare vs Base (Kimi K3)

Kimi K3

Moonshot

Flagship Base

CoT Scaler

Elo

1528

Speed

68 tok/s

Price

$15.00 / 1M

View model →vs this

MoonshotAI: Kimi K2.5

Moonshot

K2.5 Max

CoT Scaler

Elo

1515

Speed

72 tok/s

Price

$2.85 / 1M

-13 Elo-81% cost1.1x speed

Viewing now

MoonshotAI: Kimi K2 0711

Moonshot

K2 Base

Elo

1465

Speed

85 tok/s

Price

$2.30 / 1M

-63 Elo-85% cost1.3x speed

View model →vs this

Kimi Chat 1.5

Moonshot

2M Context

Elo

1420

Speed

95 tok/s

Price

$0.80 / 1M

-108 Elo-95% cost1.4x speed

View model →vs this

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.

MoonshotAI: Kimi K2.5 (9)vsQwen2.5 Coder 32B Instruct (1)

MoonshotAI: Kimi K2.5: 9W (90%)Overall: MoonshotAI: Kimi K2.5Qwen2.5 Coder 32B Instruct: 1W (10%)

← MoonshotAI: Kimi K2.5Qwen2.5 Coder 32B Instruct →

MoonshotAI: Kimi K2.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,515vs1,425

Coding Elo

1,520vs1,495

SWE-bench

71.3%vs65.2%

LiveBench

67.2%vs64.8%

GPQA Diamond

87.6%vs—

MoonshotAI: Kimi K2.5: 5WQwen2.5 Coder 32B Instruct: 0W

MoonshotAI: Kimi K2.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

72 tok/svs—

Time to first token

270 msvs—

MoonshotAI: Kimi K2.5: 2WQwen2.5 Coder 32B Instruct: 0W

MoonshotAI: Kimi K2.5 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.85/1Mvs$1/1M

Input price

$0.57/1Mvs$0.66/1M

Context window

262kvs33k

MoonshotAI: Kimi K2.5: 2WQwen2.5 Coder 32B Instruct: 1W

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 5/6

Preference Elo

MoonshotAI: Kimi K2.5

90 pts advantage

Throughput Speed

No shared data

Token Price Efficiency

Qwen2.5 Coder 32B Instruct

$1.85/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+6%1,515

EloHigh is better

1,425

Code Elo

High is better

+2%1,520

Code EloHigh is better

1,495

LiveBench

High is better

+4%67.2%

LiveBenchHigh is better

64.8%

SWE-bench

High is better

+9%71.3%

SWE-benchHigh is better

65.2%

GPQA

High is better

87.6%

GPQAHigh is better

—

TTFT

Low is better

270 ms

TTFTLow is better

—

Speed

High is better

72 tok/s

SpeedHigh is better

—

In $

Low is better

+16%$0.57 / 1M

In $Low is better

$0.66 / 1M

Out $

Low is better

$2.85 / 1M

Out $Low is better

$1.00 / 1M+185%

Context

High is better

+700%262k

ContextHigh is better

33k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 39% with Qwen2.5 Coder 32B Instruct

Monthly Volume50M tokens/mo

Quick Presets:

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

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

MoonshotAI: Kimi K2.5$62.70 / mo

In: $19.95Out: $42.75

Qwen2.5 Coder 32B Instruct$38.10 / mo

In: $23.1Out: $15

Estimated Cost Delta

Qwen2.5 Coder 32B Instruct is estimated to save $24.60/month ($295/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
MoonshotAI: Kimi K2.5:Pick MoonshotAI: Kimi K2.5 when repo-level coding accuracy is the constraint.
2
Qwen2.5 Coder 32B Instruct:Pick Qwen2.5 Coder 32B Instruct when you are optimizing output cost.
3
MoonshotAI: Kimi K2.5:Pick MoonshotAI: Kimi K2.5 as the default general assistant.

Recommended Workload Routing

Repo / coding agentsMoonshotAI: Kimi K2.5

Higher SWE-bench (71.3%).

High-volume chatQwen2.5 Coder 32B Instruct

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGMoonshotAI: Kimi K2.5

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	MoonshotAI: Kimi K2.5	Higher SWE-bench (71.3%).
High-volume chat	Qwen2.5 Coder 32B Instruct	Lower output list price ($1/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	MoonshotAI: Kimi K2.5	Larger window (262k).
Screenshots / vision	MoonshotAI: Kimi K2.5	MoonshotAI: Kimi K2.5 is the side marked multimodal in the catalog.

Community Sentiment

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CompareLLM Matrix

Verified head-to-head card

LIVE

Moonshot

MoonshotAI: Kimi K2.5

Elo 1,515

LiveBench67.2%

SWE-bench71.3%

Speed72 tok/s

Output cost$2.85/1M

Alibaba

Qwen2.5 Coder 32B Instruct

Elo 1,425

LiveBench64.8%

SWE-bench65.2%

Speed—

Output cost$1/1M

Frequently Asked Questions

Which is better overall, MoonshotAI: Kimi K2.5 or Qwen2.5 Coder 32B Instruct?: MoonshotAI: Kimi K2.5 has the higher preference Elo in our latest snapshot (1,515). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, MoonshotAI: Kimi K2.5 or Qwen2.5 Coder 32B Instruct?: MoonshotAI: Kimi K2.5 leads SWE-bench at 71.3% vs 65.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Qwen2.5 Coder 32B Instruct output tokens are $1/1M versus $2.85/1M. Input prices and retry rates still move the real bill.
Which is faster, MoonshotAI: Kimi K2.5 or Qwen2.5 Coder 32B Instruct?: We do not have TTFT for both models.
Which has the larger context window?: MoonshotAI: Kimi K2.5 accepts 262k tokens versus 33k.
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. MoonshotAI: Kimi K2.5 and Qwen2.5 Coder 32B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All MoonshotAI: Kimi K2.5 matchups →|All Qwen2.5 Coder 32B Instruct matchups →

MoonshotAI: Kimi K2.5 vs MoonshotAI: Kimi K2 0711 (previous kimi)MoonshotAI: Kimi K2.5 vs Kimi K3 (next kimi)Qwen2.5 Coder 32B Instruct vs Qwen 2.5 Plus (next qwen)Qwen 3 Max vs MoonshotAI: Kimi K2.5 Qwen QwQ 32B vs MoonshotAI: Kimi K2.5 MoonshotAI: Kimi K2 0711 vs Qwen2.5 Coder 32B Instruct

MoonshotAI: Kimi K2.5 vs Qwen2.5 Coder 32B Instruct benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

MoonshotAI: Kimi K2.5 (9)vsQwen2.5 Coder 32B Instruct (1)

MoonshotAI: Kimi K2.5: 9W (90%)Overall: MoonshotAI: Kimi K2.5Qwen2.5 Coder 32B Instruct: 1W (10%)

← MoonshotAI: Kimi K2.5Qwen2.5 Coder 32B Instruct →

MoonshotAI: Kimi K2.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,515vs1,425

Coding Elo

1,520vs1,495

SWE-bench

71.3%vs65.2%

LiveBench

67.2%vs64.8%

GPQA Diamond

87.6%vs—

MoonshotAI: Kimi K2.5: 5WQwen2.5 Coder 32B Instruct: 0W

MoonshotAI: Kimi K2.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

72 tok/svs—

Time to first token

270 msvs—

MoonshotAI: Kimi K2.5: 2WQwen2.5 Coder 32B Instruct: 0W

MoonshotAI: Kimi K2.5 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.85/1Mvs$1/1M

Input price

$0.57/1Mvs$0.66/1M

Context window

262kvs33k

MoonshotAI: Kimi K2.5: 2WQwen2.5 Coder 32B Instruct: 1W

Recommended Workload Routing

Repo / coding agentsMoonshotAI: Kimi K2.5

Higher SWE-bench (71.3%).

High-volume chatQwen2.5 Coder 32B Instruct

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGMoonshotAI: Kimi K2.5

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	MoonshotAI: Kimi K2.5	Higher SWE-bench (71.3%).
High-volume chat	Qwen2.5 Coder 32B Instruct	Lower output list price ($1/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	MoonshotAI: Kimi K2.5	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, MoonshotAI: Kimi K2.5 or Qwen2.5 Coder 32B Instruct?

MoonshotAI: Kimi K2.5 has the higher preference Elo in our latest snapshot (1,515). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, MoonshotAI: Kimi K2.5 or Qwen2.5 Coder 32B Instruct?

MoonshotAI: Kimi K2.5 leads SWE-bench at 71.3% vs 65.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Qwen2.5 Coder 32B Instruct output tokens are $1/1M versus $2.85/1M. Input prices and retry rates still move the real bill.

Which is faster, MoonshotAI: Kimi K2.5 or Qwen2.5 Coder 32B Instruct?

We do not have TTFT for both models.

Which has the larger context window?

MoonshotAI: Kimi K2.5 accepts 262k tokens versus 33k.

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. MoonshotAI: Kimi K2.5 and Qwen2.5 Coder 32B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.