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GPT-5.5 vs Kimi K2.6

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GPT-5.5 and Kimi K2.6.

OpenAI

GPT-5.5

GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...

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Moonshot AI

Kimi K2.6

Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...

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Technical Specifications

SpecificationGPT-5.5Kimi K2.6
ProviderOpenAIMoonshot AI
Context Window1,050,000 tokens262,144 tokens
Agent Suitability95/100N/A
Time to First Token (TTFT)380 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-242026-04-20

API Pricing Comparison

Input Price per Million Tokens

GPT-5.5

$5.00

Kimi K2.6

$0.66

Output Price per Million Tokens

GPT-5.5

$30.00

Kimi K2.6

$3.41

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
9420.0%vs8420.0%
GPT-5.5
Kimi K2.6
HumanEvalPython coding & logic synthesis
9680.0%vs8500.0%
GPT-5.5
Kimi K2.6
MATHComplex mathematical problem solving
9350.0%vs6400.0%
GPT-5.5
Kimi K2.6
GPQAGraduate-level expert reasoning
8420.0%vs4300.0%
GPT-5.5
Kimi K2.6
HellaSwagCommonsense reasoning and inference
9900.0%vs8500.0%
GPT-5.5
Kimi K2.6
MT-BenchMulti-turn conversation flow quality
970.0%vs890.0%
GPT-5.5
Kimi K2.6

GPT-5.5 Quirks & Gotchas

  • Best for JSON schema adherence — strict mode available via response_format parameter
  • Requires explicit tool_choice for deterministic function calling

Kimi K2.6 Quirks & Gotchas

No developer gotchas reported.