Qwen3-Coder 480B-A35B Instruct vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3-Coder 480B-A35B Instruct by Alibaba against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Qwen3-Coder 480B-A35B Instruct
Открытые веса (Open Weights)Qwen3-Coder 480B-A35B Instruct от Alibaba — передовая модель искусственного интеллекта с контекстным окном 262K токенов и стоимостью API $0/1M входящих и $0/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
56.5%
GPT-6.1 Sol
Модель глубокого рассужденияGPT-6.1 Sol от OpenAI — передовая модель искусственного интеллекта с контекстным окном 1M токенов и стоимостью API $2.0/1M входящих и $10.0/1M исходящих токенов для программирования, аналитики и автоматизации.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Ключевые отличия кратко
- Qwen3-Coder 480B-A35B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 262K tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 52 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 56.5%).
| Feature | Qwen3-Coder 480B-A35B Instruct | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 94 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 52 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 56.5% | 82.3% |
GPQA Diamond Graduate-level reasoning | 70% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
Кто лидирует в каждой категории?
At-a-glance decision frameworkРезультаты проверенных бенчмарков
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.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- Современная архитектура Code-Specialized Sparse Mixture-of-Experts Transformer от Alibaba с поддержкой до 262K токенов контекста
- Оптимальная стоимость использования ($0/1M вход, $0/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
GPT-6.1 Sol · Strengths & Key Advantages
- Современная архитектура Near-Astra unified reasoning model от OpenAI с поддержкой до 1M токенов контекста
- Оптимальная стоимость использования ($2.0/1M вход, $10.0/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3-Coder 480B-A35B Instruct | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.05 |
Output Tokens / Task Tokens generated per task | 25K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 9K | 36K |
Технические характеристики
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3-Coder 480B-A35B Instruct | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | qwen3-coder-480b-a35b-instruct | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | March 2025 | April 2026 |
| Architecture | Code-Specialized Sparse Mixture-of-Experts Transformer | Near-Astra unified reasoning model |
| Parameters | 480B total (35B active 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| 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 | 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 298 GB VRAM (4-bit Quantized) · 1032 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 | text, vision |
| Output Modalities | text | text |
Qwen3-Coder 480B-A35B Instruct vs GPT-6.1 Sol: Быстрые ответы (FAQ)
Which is more intelligent, Qwen3-Coder 480B-A35B Instruct or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 86 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 52 tokens per second.
Which model is cheaper to run?
Qwen3-Coder 480B-A35B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3-Coder 480B-A35B Instruct has lower latency, with a time to first token of 0.76s compared with 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Вердикт
Choose Qwen3-Coder 480B-A35B Instruct for корпоративной автоматизации, разработки по, ии-агентов и продуктовых решений на базе alibaba, or choose GPT-6.1 Sol for корпоративной автоматизации, разработки по, ии-агентов и продуктовых решений на базе openai.
Часто задаваемые вопросы (FAQ)
Which is cheaper: Qwen3-Coder 480B-A35B Instruct or GPT-6.1 Sol?+
Qwen3-Coder 480B-A35B Instruct costs $0/1M input and $0/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Qwen3-Coder 480B-A35B Instruct or GPT-6.1 Sol?+
Qwen3-Coder 480B-A35B Instruct supports 262K tokens whereas GPT-6.1 Sol supports 1M 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.
Официальные источники: Alibaba & OpenAI Official Documentation