Pairwise benchmark snapshot · Aug 1, 2026

Kimi Chat 1.5 vs Qwen QwQ 32B benchmark

Name: Kimi Chat 1.5 vs Qwen QwQ 32B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Kimi Chat 1.5 (Moonshot) and Qwen QwQ 32B (Alibaba) using the latest snapshots we have as of Aug 1, 2026. On preference Elo, Qwen QwQ 32B is ahead by 75 points (1,495 vs 1,420; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Qwen QwQ 32B resolves 67.5% versus 51% (seed-bootstrap (Aug 1, 2026)). Kimi Chat 1.5 streams faster (95 tok/s) while Kimi Chat 1.5 is the cheaper output token ($0.8/1M). Kimi Chat 1.5 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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Moonshot

Kimi Chat 1.5

Moonshot ultra-long context model supporting up to 2 million tokens per request.

Elo 1,420$0.8/1M out

Alibaba

Qwen QwQ 32B

Alibaba specialized open reasoning model competing with frontier closed reasoning models.

Elo 1,495$1.2/1M 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.

Kimi Chat 1.5 (5)vsQwen QwQ 32B (5)

Kimi Chat 1.5: 5W (50%)Overall: Even matchupQwen QwQ 32B: 5W (50%)

← Kimi Chat 1.5Qwen QwQ 32B →

Qwen QwQ 32B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,420vs1,495

Coding Elo

1,390vs1,520

SWE-bench

51%vs67.5%

LiveBench

58%vs68%

GPQA Diamond

71.5%vs83.2%

Kimi Chat 1.5: 0WQwen QwQ 32B: 5W

Kimi Chat 1.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

95 tok/svs68 tok/s

Time to first token

210 msvs320 ms

Kimi Chat 1.5: 2WQwen QwQ 32B: 0W

Kimi Chat 1.5 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.8/1Mvs$1.2/1M

Input price

$0.2/1Mvs$0.3/1M

Context window

2Mvs131k

Kimi Chat 1.5: 3WQwen QwQ 32B: 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

Qwen QwQ 32B

75 pts advantage

Throughput Speed

Kimi Chat 1.5

27 tok/s faster

Price Efficiency

Kimi Chat 1.5

$0.40/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,420

EloHigh is better

1,495+5%

Code Elo

High is better

1,390

Code EloHigh is better

1,520+9%

LiveBench

High is better

58%

LiveBenchHigh is better

68%+17%

SWE-bench

High is better

51%

SWE-benchHigh is better

67.5%+32%

GPQA

High is better

71.5%

GPQAHigh is better

83.2%+16%

TTFT

Low is better

+52%210 ms

TTFTLow is better

320 ms

Speed

High is better

+40%95 tok/s

SpeedHigh is better

68 tok/s

In $

Low is better

+50%$0.2/1M

In $Low is better

$0.3/1M

Out $

Low is better

+50%$0.8/1M

Out $Low is better

$1.2/1M

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 33% with Kimi Chat 1.5

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Kimi Chat 1.5$19.00 / mo

In: $7.00Out: $12.00

Qwen QwQ 32B$28.50 / mo

In: $10.50Out: $18.00

Estimated Cost Delta

Kimi Chat 1.5 is estimated to save $9.50/month ($114/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen QwQ 32B:Pick Qwen QwQ 32B when repo-level coding accuracy is the constraint.
2
Kimi Chat 1.5:Pick Kimi Chat 1.5 when you are optimizing output cost.
3
Kimi Chat 1.5:Pick Kimi Chat 1.5 when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsQwen QwQ 32B

Higher SWE-bench (67.5%).

High-volume chatKimi Chat 1.5

Lower output list price ($0.8/1M).

Voice / low-latency UIKimi Chat 1.5

Lower TTFT (210 ms).

Long-document RAGKimi Chat 1.5

Larger window (2M).

Screenshots / visionQwen QwQ 32B

Qwen QwQ 32B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen QwQ 32B	Higher SWE-bench (67.5%).
High-volume chat	Kimi Chat 1.5	Lower output list price ($0.8/1M).
Voice / low-latency UI	Kimi Chat 1.5	Lower TTFT (210 ms).
Long-document RAG	Kimi Chat 1.5	Larger window (2M).
Screenshots / vision	Qwen QwQ 32B	Qwen QwQ 32B is the side marked multimodal in the catalog.

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LIVE

Moonshot

Kimi Chat 1.5

Elo 1,420

LiveBench58%

SWE-bench51%

Speed95 tok/s

Output cost$0.8/1M

Alibaba

Qwen QwQ 32B

Elo 1,495

LiveBench68%

SWE-bench67.5%

Speed68 tok/s

Output cost$1.2/1M

Frequently Asked Questions

Which is better overall, Kimi Chat 1.5 or Qwen QwQ 32B?: Qwen QwQ 32B has the higher preference Elo in our latest snapshot (1,495). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Kimi Chat 1.5 or Qwen QwQ 32B?: Qwen QwQ 32B leads SWE-bench at 67.5% vs 51%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Kimi Chat 1.5 output tokens are $0.8/1M versus $1.2/1M. Input prices and retry rates still move the real bill.
Which is faster, Kimi Chat 1.5 or Qwen QwQ 32B?: Kimi Chat 1.5 has the lower time-to-first-token (210 ms vs 320 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Kimi Chat 1.5 accepts 2M tokens versus 131k.
Can I self-host either model?: Qwen QwQ 32B is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
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. Kimi Chat 1.5 and Qwen QwQ 32B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Kimi Chat 1.5 matchups →|All Qwen QwQ 32B matchups →

Kimi Chat 1.5 vs MoonshotAI: Kimi K2 0711 (next kimi)Qwen QwQ 32B vs Qwen3 235B (previous qwen)Qwen QwQ 32B vs Qwen 3 Max (next qwen)Kimi K3 vs Qwen QwQ 32B Qwen 2.5 Coder 32B vs Kimi Chat 1.5 Qwen3 235B vs Kimi Chat 1.5

Kimi Chat 1.5 vs Qwen QwQ 32B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Kimi Chat 1.5 (5)vsQwen QwQ 32B (5)

Kimi Chat 1.5: 5W (50%)Overall: Even matchupQwen QwQ 32B: 5W (50%)

← Kimi Chat 1.5Qwen QwQ 32B →

Qwen QwQ 32B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,420vs1,495

Coding Elo

1,390vs1,520

SWE-bench

51%vs67.5%

LiveBench

58%vs68%

GPQA Diamond

71.5%vs83.2%

Kimi Chat 1.5: 0WQwen QwQ 32B: 5W

Kimi Chat 1.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

95 tok/svs68 tok/s

Time to first token

210 msvs320 ms

Kimi Chat 1.5: 2WQwen QwQ 32B: 0W

Kimi Chat 1.5 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.8/1Mvs$1.2/1M

Input price

$0.2/1Mvs$0.3/1M

Context window

2Mvs131k

Kimi Chat 1.5: 3WQwen QwQ 32B: 0W

Recommended Workload Routing

Repo / coding agentsQwen QwQ 32B

Higher SWE-bench (67.5%).

High-volume chatKimi Chat 1.5

Lower output list price ($0.8/1M).

Voice / low-latency UIKimi Chat 1.5

Lower TTFT (210 ms).

Long-document RAGKimi Chat 1.5

Larger window (2M).

Screenshots / visionQwen QwQ 32B

Qwen QwQ 32B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen QwQ 32B	Higher SWE-bench (67.5%).
High-volume chat	Kimi Chat 1.5	Lower output list price ($0.8/1M).
Voice / low-latency UI	Kimi Chat 1.5	Lower TTFT (210 ms).
Long-document RAG	Kimi Chat 1.5	Larger window (2M).
Screenshots / vision	Qwen QwQ 32B	Qwen QwQ 32B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Kimi Chat 1.5 or Qwen QwQ 32B?

Qwen QwQ 32B has the higher preference Elo in our latest snapshot (1,495). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Kimi Chat 1.5 or Qwen QwQ 32B?

Qwen QwQ 32B leads SWE-bench at 67.5% vs 51%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Kimi Chat 1.5 output tokens are $0.8/1M versus $1.2/1M. Input prices and retry rates still move the real bill.

Which is faster, Kimi Chat 1.5 or Qwen QwQ 32B?

Kimi Chat 1.5 has the lower time-to-first-token (210 ms vs 320 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Kimi Chat 1.5 accepts 2M tokens versus 131k.

Can I self-host either model?

Qwen QwQ 32B is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

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. Kimi Chat 1.5 and Qwen QwQ 32B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.