Pairwise benchmark snapshot · Aug 1, 2026

GPT-4.5 Orion vs Kimi Chat 1.5 benchmark

Name: GPT-4.5 Orion vs Kimi Chat 1.5 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GPT-4.5 Orion (OpenAI) and Kimi Chat 1.5 (Moonshot) using the latest snapshots we have as of Aug 1, 2026. On preference Elo, GPT-4.5 Orion is ahead by 120 points (1,540 vs 1,420; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, GPT-4.5 Orion resolves 71% 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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OpenAI

GPT-4.5 Orion

OpenAI largest dense non-reasoning model with expansive world knowledge and reduced hallucinations.

Elo 1,540$150/1M out

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

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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.

GPT-4.5 Orion (5)vsKimi Chat 1.5 (5)

GPT-4.5 Orion: 5W (50%)Overall: Even matchupKimi Chat 1.5: 5W (50%)

← GPT-4.5 OrionKimi Chat 1.5 →

GPT-4.5 Orion (5/5)

Intelligence & Reasoning

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

Preference Elo

1,540vs1,420

Coding Elo

1,520vs1,390

SWE-bench

71%vs51%

LiveBench

68.5%vs58%

GPQA Diamond

84%vs71.5%

GPT-4.5 Orion: 5WKimi Chat 1.5: 0W

Kimi Chat 1.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

60 tok/svs95 tok/s

Time to first token

350 msvs210 ms

GPT-4.5 Orion: 0WKimi Chat 1.5: 2W

Kimi Chat 1.5 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$150/1Mvs$0.8/1M

Input price

$75/1Mvs$0.2/1M

Context window

128kvs2M

GPT-4.5 Orion: 0WKimi Chat 1.5: 3W

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

GPT-4.5 Orion

120 pts advantage

Throughput Speed

Kimi Chat 1.5

35 tok/s faster

Price Efficiency

Kimi Chat 1.5

$149.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+8%1,540

EloHigh is better

1,420

Code Elo

High is better

+9%1,520

Code EloHigh is better

1,390

LiveBench

High is better

+18%68.5%

LiveBenchHigh is better

58%

SWE-bench

High is better

+39%71%

SWE-benchHigh is better

51%

GPQA

High is better

+17%84%

GPQAHigh is better

71.5%

TTFT

Low is better

350 ms

TTFTLow is better

210 ms+67%

Speed

High is better

60 tok/s

SpeedHigh is better

95 tok/s+58%

In $

Low is better

$75/1M

In $Low is better

$0.2/1M+37400%

Out $

Low is better

$150/1M

Out $Low is better

$0.8/1M+18650%

Context

High is better

128k

ContextHigh is better

2M+1463%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 100% 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)

GPT-4.5 Orion$4,875.00 / mo

In: $2625.00Out: $2250.00

Kimi Chat 1.5$19.00 / mo

In: $7.00Out: $12.00

Estimated Cost Delta

Kimi Chat 1.5 is estimated to save $4,856.00/month ($58,272/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GPT-4.5 Orion:Pick GPT-4.5 Orion 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 agentsGPT-4.5 Orion

Higher SWE-bench (71%).

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 / visionGPT-4.5 Orion

GPT-4.5 Orion is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GPT-4.5 Orion	Higher SWE-bench (71%).
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	GPT-4.5 Orion	GPT-4.5 Orion is the side marked multimodal in the catalog.

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LIVE

OpenAI

GPT-4.5 Orion

Elo 1,540

LiveBench68.5%

SWE-bench71%

Speed60 tok/s

Output cost$150/1M

Moonshot

Kimi Chat 1.5

Elo 1,420

LiveBench58%

SWE-bench51%

Speed95 tok/s

Output cost$0.8/1M

Frequently Asked Questions

Which is better overall, GPT-4.5 Orion or Kimi Chat 1.5?: GPT-4.5 Orion has the higher preference Elo in our latest snapshot (1,540). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, GPT-4.5 Orion or Kimi Chat 1.5?: GPT-4.5 Orion leads SWE-bench at 71% 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 $150/1M. Input prices and retry rates still move the real bill.
Which is faster, GPT-4.5 Orion or Kimi Chat 1.5?: Kimi Chat 1.5 has the lower time-to-first-token (210 ms vs 350 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 128k.
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. GPT-4.5 Orion and Kimi Chat 1.5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All GPT-4.5 Orion matchups →|All Kimi Chat 1.5 matchups →

GPT-4.5 Orion vs OpenAI: o1 (previous gpt-flagship)GPT-4.5 Orion vs GPT-5 (next gpt-flagship)Kimi Chat 1.5 vs MoonshotAI: Kimi K2 0711 (next kimi)GPT-5.6 Terra vs GPT-4.5 Orion Kimi K3 vs GPT-4.5 Orion GPT-5.6 Sol vs GPT-4.5 Orion

GPT-4.5 Orion vs Kimi Chat 1.5 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GPT-4.5 Orion (5)vsKimi Chat 1.5 (5)

GPT-4.5 Orion: 5W (50%)Overall: Even matchupKimi Chat 1.5: 5W (50%)

← GPT-4.5 OrionKimi Chat 1.5 →

GPT-4.5 Orion (5/5)

Intelligence & Reasoning

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

Preference Elo

1,540vs1,420

Coding Elo

1,520vs1,390

SWE-bench

71%vs51%

LiveBench

68.5%vs58%

GPQA Diamond

84%vs71.5%

GPT-4.5 Orion: 5WKimi Chat 1.5: 0W

Kimi Chat 1.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

60 tok/svs95 tok/s

Time to first token

350 msvs210 ms

GPT-4.5 Orion: 0WKimi Chat 1.5: 2W

Kimi Chat 1.5 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$150/1Mvs$0.8/1M

Input price

$75/1Mvs$0.2/1M

Context window

128kvs2M

GPT-4.5 Orion: 0WKimi Chat 1.5: 3W

Recommended Workload Routing

Repo / coding agentsGPT-4.5 Orion

Higher SWE-bench (71%).

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 / visionGPT-4.5 Orion

GPT-4.5 Orion is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GPT-4.5 Orion	Higher SWE-bench (71%).
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	GPT-4.5 Orion	GPT-4.5 Orion is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GPT-4.5 Orion or Kimi Chat 1.5?

GPT-4.5 Orion has the higher preference Elo in our latest snapshot (1,540). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, GPT-4.5 Orion or Kimi Chat 1.5?

GPT-4.5 Orion leads SWE-bench at 71% 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 $150/1M. Input prices and retry rates still move the real bill.

Which is faster, GPT-4.5 Orion or Kimi Chat 1.5?

Kimi Chat 1.5 has the lower time-to-first-token (210 ms vs 350 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 128k.

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. GPT-4.5 Orion and Kimi Chat 1.5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.