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Head-to-Head Model Comparison

Kimi K3 vs Aya Expanse 32B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs अनुसंधान दल द्वारा समीक्षितअंतिम समीक्षा: October 2026संपादकीय नीति
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Moonshot AICohereflagshipopen-source

Compare Kimi K3 by Moonshot AI against Aya Expanse 32B by Cohere across real-world coding, reasoning, token economics, and latency.

आमने-सामने मॉडल तुलना (VS)

किसी भी मॉडल को बदलें और तुलना करें

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

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Live comparison: no in-depth editorial review has been published for this pair yet.

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Moonshot AI

Kimi K3

ओपन-वेट्स मॉडल

Moonshot AI द्वारा विकसित Kimi K3 एक उन्नत AI मॉडल है, जिसमें 1.0M टोकन का कॉन्टेक्स्ट विंडो और $0/1M इनपुट तथा $0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index93 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.5%

Cohere

Aya Expanse 32B

ओपन-वेट्स मॉडल

Cohere द्वारा विकसित Aya Expanse 32B एक उन्नत AI मॉडल है, जिसमें 128K टोकन का कॉन्टेक्स्ट विंडो और $0.25/1M इनपुट तथा $0.5/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index44 / 100
Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

33.5%

एक नज़र में मुख्य अंतर

  • Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
  • Kimi K3 has a larger context window (1M vs 128K tokens).
  • Aya Expanse 32B is faster at 85 tok/s vs 58 tok/s.
  • Kimi K3 leads on SWE-bench coding (74.5% vs 33.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureKimi K3Aya Expanse 32B
Intelligence Index
Composite reasoning & knowledge score (0-100)
9344
Provider
Moonshot AICohere
Input Price
Cost per million input tokens
$0.00 / 1M$0.25 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.50 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$0.14 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
131K8K
Throughput Speed
Tokens generated per second
58 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
74.5%33.5%
GPQA Diamond
Graduate-level reasoning
79%50%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.06 / 1M

किस क्षेत्र में कौन आगे है?

At-a-glance decision framework
Coding
Kimi K3
+41.0% SWE-bench lead
Cost
Kimi K3
1.0x cheaper monthly runtime
Speed
Aya Expanse 32B
+47% faster generation
Context
Kimi K3
1.05M tokens window

सत्यापित बेंचमार्क स्कोर तुलना

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

Kimi K3Aya Expanse 32B
SWE-bench Verified (Autonomous Coding)

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

74.5%vs33.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

79%vs50%
MMLU-Pro (Multi-task Language Understanding)

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

88.8%vs73%
LMSYS Chatbot Arena Elo

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

1480ptsvs1265pts
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs72%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

58 tok/s vs85 tok/s

लेटेंसी और रिस्पॉन्स समय

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 K30.72s
Aya Expanse 32B0.44s
Est. End-to-End Response (500 tokens)TTFT + generation time
Kimi K39.34s
Aya Expanse 32B6.32s

रियल-टाइम टोकन लागत सिम्युलेटर

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Kimi K3 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Aya Expanse 32B Monthly API Bill
$0.92 / month
$0.25/1M in · $0.5/1M out
Kimi K3 saves approximately $0.92 / month (100%) under this workload.1.0x Savings

Kimi K3 · Strengths & Key Advantages

  • Moonshot AI की उन्नत Colossus-Scale Multimodal Sparse Mixture-of-Experts आर्किटेक्चर और 1.0M कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($0/1M इनपुट, $0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Aya Expanse 32B · Strengths & Key Advantages

  • Cohere की उन्नत High-Capacity Multilingual Transformer आर्किटेक्चर और 128K कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($0.25/1M इनपुट, $0.5/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Token Use & Task Economics

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

MetricKimi K3Aya Expanse 32B
Cost per Task
Estimated USD to finish one task
$2.00$0.18
Output Tokens / Task
Tokens generated per task
35K28K
Reasoning Tokens / Task
Thinking tokens burned per task
18K0K

तकनीकी विशिष्टताएँ (Specs)

Architecture, release timeline, capabilities, and modalities.

FeatureKimi K3Aya Expanse 32B
Official API Model ID (SDK)kimi-k3aya-expanse-32b
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-072024-10
Knowledge CutoffMarch 2026August 2024
ArchitectureColossus-Scale Multimodal Sparse Mixture-of-ExpertsHigh-Capacity Multilingual Transformer
Parameters2.8T total (MoE)32B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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)Flat rate ($0.25/1M in) across full 128K window
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Cached input at $0.06/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Kimi K3 vs Aya Expanse 32B: त्वरित प्रश्नोत्तर (FAQ)

Which is more intelligent, Kimi K3 or Aya Expanse 32B?

Kimi K3 is more intelligent. It scores 93 compared with 44 on the composite Intelligence Index.

Which model is faster?

Aya Expanse 32B is faster, generating 85 tokens per second compared with 58 tokens per second.

Which model is cheaper to run?

Kimi K3 is cheaper at $0.00 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Aya Expanse 32B has lower latency, with a time to first token of 0.44s compared with 0.72s.

Which model has a larger context window?

Kimi K3 has a larger context window, supporting 1.05M tokens compared with 128K tokens.

निष्कर्ष

Choose Kimi K3 for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और moonshot ai आधारित प्रोडक्शन सिस्टम, or choose Aya Expanse 32B for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और cohere आधारित प्रोडक्शन सिस्टम.

अक्सर पूछे जाने वाले प्रश्न (FAQ)

Which is cheaper: Kimi K3 or Aya Expanse 32B?+

Kimi K3 costs $0/1M input and $0/1M output tokens, while Aya Expanse 32B costs $0.25/1M input and $0.5/1M output tokens.

Which model has a larger context window: Kimi K3 or Aya Expanse 32B?+

Kimi K3 supports 1.0M tokens whereas Aya Expanse 32B supports 128K tokens.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

स्रोत: Moonshot AI & Cohere Official Documentation