Model profile
kwaipilot

Kwaipilot: KAT-Coder-Pro V1

Kwaipilot: KAT-Coder-Pro V1 is a budget text-first model from kwaipilot with a balanced runtime profile, large context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsBalanced latencyLarge contextBudget pricing
Intelligence
36.0

Benchmark blend

Coding
18.3

Dev workflow signal

Context
256K Tokens

Large

Input Price
$0.30

Budget tier

Decision snapshot
53

Kwaipilot: KAT-Coder-Pro V1 currently reads as a budget text-first option with large context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Balanced
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
36.0
36

General reasoning and benchmark headroom.

Limited
Speed
59 tok/s
57

TTFT 1.37s

Situational
Context
256K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.30
86

$1.20 output / 1M

Efficient

Editorial Profile

Kwaipilot: KAT-Coder-Pro V1 in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 18Math score 95

KAT-Coder-Pro V1 is KwaiKAT's most advanced agentic coding model in the KAT-Coder series. Designed specifically for agentic coding tasks, it excels in real-world software engineering scenarios, achieving 73.4% solve rate on the SWE-Bench Verified benchmark. The model has been optimized for tool-use capability, multi-turn interaction, instruction following, generalization, and comprehensive capabilities through a multi-stage training process, including mid-training, supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and scalable agentic RL.

Identity

kwaipilot text-first profile

Positioning

Long-context research / Agent workflows with large context and balanced runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
36.0
MMLU Pro
81.3%
GPQA
76.4%
HLE
33.4%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
18.3
LiveCodeBench
0.747
SciCode
36.6%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
94.7
AIME 2025
94.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
68.4%
TAU2
88.6%
TerminalBench Hard
9.1%
LCR
74.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
256K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Other
Max Completion
128000
Moderation
No
Supported Parameters
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.30
Output
per 1M output tokens
$1.20
Blended
AA 3:1 mix
$0.53

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.