โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

Gemini 3.1 Flash vs Kimi K2.7 Code

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 Gemini 3.1 Flash and Kimi K2.7 Code.

Google

Gemini 3.1 Flash

Gemini 3.1 Flash is Google's high-speed, cost-efficient multimodal model in the 3.1 generation, purpose-built for high-volume content synthesis, classification, and intelligent routing at scale. Featuring a 1-million-token context window, it can process large batches of documents, customer data, or multimedia content in a single inference pass, dramatically reducing pipeline complexity. At just $0.25/MTok for input, it is one of the most affordable routes to Google-caliber multimodal AI, making it an ideal backbone for production pipelines, data enrichment workflows, and high-frequency API integrations.

View Full Specs
Moonshot AI

Kimi K2.7 Code

MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts...

View Full Specs

Technical Specifications

SpecificationGemini 3.1 FlashKimi K2.7 Code
ProviderGoogleMoonshot AI
Context Window1,000,000 tokens262,144 tokens
Agent Suitability86/100N/A
Time to First Token (TTFT)150 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-202026-06-12

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.1 Flash

$0.25

Kimi K2.7 Code

$0.74

Output Price per Million Tokens

Gemini 3.1 Flash

$1.50

Kimi K2.7 Code

$3.50

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
8680.0%vs8500.0%
Gemini 3.1 Flash
Kimi K2.7 Code
HumanEvalPython coding & logic synthesis
8850.0%vs9320.0%
Gemini 3.1 Flash
Kimi K2.7 Code
MATHComplex mathematical problem solving
7820.0%vs7650.0%
Gemini 3.1 Flash
Kimi K2.7 Code
GPQAGraduate-level expert reasoning
6050.0%vs4600.0%
Gemini 3.1 Flash
Kimi K2.7 Code
HellaSwagCommonsense reasoning and inference
9520.0%vs8600.0%
Gemini 3.1 Flash
Kimi K2.7 Code
MT-BenchMulti-turn conversation flow quality
900.0%vs900.0%
Gemini 3.1 Flash
Kimi K2.7 Code

Gemini 3.1 Flash Quirks & Gotchas

  • โ–ธMost cost-effective Google model โ€” ideal for high-volume pipelines
  • โ–ธContext caching available via Vertex AI for repeated document processing

Kimi K2.7 Code Quirks & Gotchas

No developer gotchas reported.