GPT-3.5 Turbo vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-3.5 Turbo by OpenAI against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
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GPT-3.5 Turbo
ChatGPT RevolutionThe original engine behind the global ChatGPT phenomenon. Highly optimized for conversational dialogue, rapid response, and cost-effective API deployment.
$0.50 / 1M
$1.50 / 1M
16.4K tokens
12.5%
Kimi K3
2.8T Open ReasonerMonumental 2.8-trillion parameter open-weights multimodal frontier model featuring 1M context window and toggleable max-effort thinking.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Key Differences at a Glance
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 16K tokens).
- GPT-3.5 Turbo is faster at 90 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 12.5%).
| Feature | GPT-3.5 Turbo | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 30 | 93 |
Provider | OpenAI | Moonshot AI |
Input Price Cost per million input tokens | $0.50 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $1.50 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.42 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 16.4K tokens~25 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 4K | 131K |
Throughput Speed Tokens generated per second | 90 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 12.5% | 74.5% |
GPQA Diamond Graduate-level reasoning | 28% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.00 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-3.5 Turbo · Strengths & Key Advantages
- Accessible, reliable conversational assistant that popularized generative AI globally
- 10x cost reduction compared to legacy GPT-3 Davinci
- Fast inference and broad developer integration
Considerations & Trade-offs
- Lacks vision and native multimodal understanding
- Lower reasoning and coding benchmark scores compared to GPT-4 class models
Kimi K3 · Strengths & Key Advantages
- Largest open-weights foundation model in existence (2.8T parameters)
- 1480 LMSYS Chatbot Arena Elo rivaling proprietary closed frontier leaders
- Full 1M token context window paired with massive 131K max output tokens
Considerations & Trade-offs
- Extreme hardware prerequisites (distributed multi-node cluster deployment)
- Generates extensive reasoning traces that demand careful token budget management
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-3.5 Turbo | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $2.00 |
Output Tokens / Task Tokens generated per task | 24K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 18K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-3.5 Turbo | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-3-5-turbo | kimi-k3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2022-11 | 2026-07 |
| Knowledge Cutoff | September 2021 | March 2026 |
| Architecture | Transformer with RLHF & PPO | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential (est. 20B MoE / 175B base) | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.5/1M in) across full 16K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.25/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
GPT-3.5 Turbo vs Kimi K3: Quick Answers
Which is more intelligent, GPT-3.5 Turbo or Kimi K3?
Kimi K3 is more intelligent. It scores 93 compared with 30 on the composite Intelligence Index.
Which model is faster?
GPT-3.5 Turbo is faster, generating 90 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 $0.42 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-3.5 Turbo has lower latency, with a time to first token of 0.45s compared with 0.72s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 16.4K tokens.
Verdict
Choose GPT-3.5 Turbo for everyday conversational chatbots, customer support routing, and high-volume text summarization, or choose Kimi K3 for national ai research facilities, sovereign enterprise clouds, and frontier stem reasoning.
Frequently asked questions
Which is cheaper: GPT-3.5 Turbo or Kimi K3?+
GPT-3.5 Turbo costs $0.5/1M input and $1.5/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-3.5 Turbo or Kimi K3?+
GPT-3.5 Turbo supports 16K tokens whereas Kimi K3 supports 1.0M tokens.
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.
Sources: OpenAI & Moonshot AI Official Documentation