Kimi K3 vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Kimi K3 by Moonshot AI and GPT-6 Astra by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Kimi K3 holds a notable pricing advantage for token ingestion. On output generation, Kimi K3 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Kimi K3 provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, GPT-6 Astra leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
किसी भी मॉडल को बदलें और तुलना करें
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Kimi K3
ओपन-वेट्स मॉडलMoonshot 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।
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
GPT-6 Astra
डीप रीजनिंग मॉडलOpenAI द्वारा विकसित GPT-6 Astra एक उन्नत AI मॉडल है, जिसमें 1M टोकन का कॉन्टेक्स्ट विंडो और $10.0/1M इनपुट तथा $50.0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
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 272K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 58 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 74.5%).
| Feature | Kimi K3 | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 96 |
Provider | Moonshot AI | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 131K | 128K |
Throughput Speed Tokens generated per second | 58 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 74.5% | 82.4% |
GPQA Diamond Graduate-level reasoning | 79% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 1M |
किस क्षेत्र में कौन आगे है?
At-a-glance decision frameworkसत्यापित बेंचमार्क स्कोर तुलना
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
लेटेंसी और रिस्पॉन्स समय
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
रियल-टाइम टोकन लागत सिम्युलेटर
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
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
GPT-6 Astra · Strengths & Key Advantages
- OpenAI की उन्नत Omni-Adaptive Autoregressive with Dynamic CoT आर्किटेक्चर और 1M कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($10.0/1M इनपुट, $50.0/1M 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Kimi K3 | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 35K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 32K |
तकनीकी विशिष्टताएँ (Specs)
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K3 | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | kimi-k3 | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | March 2026 | June 2026 |
| Architecture | Colossus-Scale Multimodal Sparse Mixture-of-Experts | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 2.8T total (MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision, audio |
| Output Modalities | text | text, audio |
Kimi K3 vs GPT-6 Astra: त्वरित प्रश्नोत्तर (FAQ)
Which is more intelligent, Kimi K3 or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 93 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 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 $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Kimi K3 has lower latency, with a time to first token of 0.72s compared with 0.95s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 272K tokens.
निष्कर्ष
Verdict & Model Selection Recommendation: Choose Kimi K3 if your priority is National AI research facilities, sovereign enterprise clouds, and frontier STEM reasoning. Its performance in demanding tasks justifies its operational token cost. Choose GPT-6 Astra if your workflow prioritizes Autonomous browser/computer workflows, real-time voice agents, and high-difficulty mathematical reasoning. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
अक्सर पूछे जाने वाले प्रश्न (FAQ)
How does Kimi K3 compare to GPT-6 Astra in API token pricing and cost per token?+
Kimi K3 costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas GPT-6 Astra costs $10.00 input and $50.00 output. Overall, Kimi K3 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Kimi K3 or GPT-6 Astra?+
Kimi K3 supports a context window of 1,049K tokens (~786k words), while GPT-6 Astra supports 272K tokens (~204k words). Therefore, Kimi K3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Kimi K3 or GPT-6 Astra?+
Kimi K3 scores 74.5% on SWE-bench Verified while GPT-6 Astra scores 82.4%. For autonomous software engineering and multi-file debugging, GPT-6 Astra holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Kimi K3 and GPT-6 Astra?+
Kimi K3 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-6 Astra provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Kimi K3 and GPT-6 Astra?+
Kimi K3 generates approximately 58 tokens per second, while GPT-6 Astra delivers 100 tokens per second. In latency-sensitive production environments, GPT-6 Astra provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Kimi K3 and GPT-6 Astra to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Kimi K3 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-6 Astra. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: GetInfoUs Independent AI Benchmarks & Official API Specs