Head-to-Head Model Comparison

Kimi K2.5 vs Kimi K2 Thinking

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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Kimi K2.5Profile

Kimi K2 ThinkingProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Moonshot AI

Kimi K2.5

IA Multimodale Nativa

Kimi K2.5 di Moonshot AI è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 262K, velocità di 65 token/s, indice di intelligenza di 86/100 e

Intelligence Index86 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

52%

Moonshot AI

Kimi K2 Thinking

Ragionamento a Pesi Aperti

Kimi K2 Thinking di Moonshot AI è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 262K, velocità di 55 token/s, indice di intelligenza di 85/100 e ta

Intelligence Index85 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

50%

Differenze Chiave a Colpo d’Occhio

Both models cost the same under this workload — choose on capability, not price.
Kimi K2.5 is faster at 65 tok/s vs 55 tok/s.
Kimi K2.5 leads on SWE-bench coding (52.0% vs 50.0%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Kimi K2.5	Kimi K2 Thinking
Intelligence Index Composite reasoning & knowledge score (0-100)	86	85
Provider	Moonshot AI	Moonshot AI
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	16K	32K
Throughput Speed Tokens generated per second	65 tok/s	55 tok/s
SWE-bench Verified Real-world software engineering	52%	50%
GPQA Diamond Graduate-level reasoning	69.5%	70%

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Kimi K2.5

+2.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Kimi K2.5

+18% faster generation

Context

Tied

262.1K tokens window

Punteggi Benchmark Verificati

Official evaluation metrics verified by independent third-party research labs.

Kimi K2.5Kimi K2 Thinking

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

52%vs50%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

69.5%vs70%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

82%vs82.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1380ptsvs1375pts

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs82.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs55 tok/s

Latenza e Tempo di Risposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Kimi K2.50.62s

Kimi K2 Thinking0.95s

Est. End-to-End Response (500 tokens)TTFT + generation time

Kimi K2.58.31s

Kimi K2 Thinking10.04s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Kimi K2.5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Kimi K2 Thinking Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Kimi K2.5 · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 16K token (SWE-bench Verified 52% · GPQA Diamond 69.5%)
Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 620 ms
Pesi aperti (1.2T total (36B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Kimi K2 Thinking · Strengths & Key Advantages

Finestra di contesto da 262K con capacità massima di output fino a 32K token (SWE-bench Verified 50% · GPQA Diamond 70%)
Throughput elevato di 55 token/secondo con latenza al primo token (TTFT) di soli 950 ms
Pesi aperti (1T total (32B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Kimi K2.5	Kimi K2 Thinking
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	24K	28K
Reasoning Tokens / Task Thinking tokens burned per task	8K	16K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Kimi K2.5	Kimi K2 Thinking
Official API Model ID (SDK)	kimi-k2-5	kimi-k2-thinking
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-01	2025-11
Knowledge Cutoff	November 2025	September 2025
Architecture	Multimodal Open Mixture-of-Experts Transformer	Deliberate Reasoning Open Sparse Mixture-of-Experts
Parameters	1.2T total (36B active MoE)	1T total (32B active MoE)
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 744 GB VRAM (4-bit Quantized) · 2580 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 620 GB VRAM (4-bit Quantized) · 2150 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text
Output Modalities	text	text

Kimi K2.5 vs Kimi K2 Thinking: Risposte Rapide (FAQ)

Which is more intelligent, Kimi K2.5 or Kimi K2 Thinking?

Kimi K2.5 is more intelligent. It scores 86 compared with 85 on the composite Intelligence Index.

Which model is faster?

Kimi K2.5 is faster, generating 65 tokens per second compared with 55 tokens per second.

Which model has lower latency?

Kimi K2.5 has lower latency, with a time to first token of 0.62s compared with 0.95s.

Verdetto

Sia Kimi K2.5 (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) sia Kimi K2 Thinking (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Kimi K2.5 guida nei benchmark di ragionamento e programmazione, Kimi K2.5 garantisce una generazione più rapida e Kimi K2.5 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Kimi K2.5 o Kimi K2 Thinking?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Kimi K2.5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Kimi K2.5 e Kimi K2 Thinking?+

Kimi K2.5 costa $0.00 / $0.00 per milione di token di input/output, mentre Kimi K2 Thinking costa $0.00 / $0.00. Nel costo combinato, Kimi K2.5 risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Kimi K2.5 supporta 262.1K token di contesto contro 262.1K token di Kimi K2 Thinking, mentre Kimi K2.5 offre una velocità di generazione superiore.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Kimi K2.5 vs Kimi K2 Thinking

Cambia e Confronta Qualsiasi Modello

Kimi K2.5

Kimi K2 Thinking

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Kimi K2.5 · Strengths & Key Advantages

Considerations & Trade-offs

Kimi K2 Thinking · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Kimi K2.5 vs Kimi K2 Thinking: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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