Qwen 2.5 7B Instruct vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen 2.5 7B Instruct by Alibaba against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Qwen 2.5 7B Instruct
Modelo de Pesos AbertosO Qwen 2.5 7B Instruct da Alibaba é um modelo de inteligência artificial com janela de contexto de 131K tokens e preço de API de $0.06/1M na entrada e $0.12/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.06 / 1M
$0.12 / 1M
131.1K tokens
28.5%
GPT-6.1 Sol
Modelo de Raciocínio ProfundoO GPT-6.1 Sol da OpenAI é um modelo de inteligência artificial com janela de contexto de 1M tokens e preço de API de $2.0/1M na entrada e $10.0/1M na saída, projetado para programação, raciocínio e automação empresarial.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Principais Diferenças em Resumo
- Qwen 2.5 7B Instruct is 56.3x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 131K tokens).
- Qwen 2.5 7B Instruct is faster at 130 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 28.5%).
| Feature | Qwen 2.5 7B Instruct | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 35 | 94 |
Provider | Alibaba | OpenAI |
Input Price Cost per million input tokens | $0.06 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.12 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 130 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 28.5% | 82.3% |
GPQA Diamond Graduate-level reasoning | 36.8% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen 2.5 7B Instruct · Strengths & Key Advantages
- Arquitetura Dense Autoregressive Transformer avançada da Alibaba com contexto de 131K tokens
- Excelente relação custo-benefício ($0.06/1M entrada, $0.12/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
GPT-6.1 Sol · Strengths & Key Advantages
- Arquitetura Near-Astra unified reasoning model avançada da OpenAI com contexto de 1M tokens
- Excelente relação custo-benefício ($2.0/1M entrada, $10.0/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen 2.5 7B Instruct | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $2.05 |
Output Tokens / Task Tokens generated per task | 20K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen 2.5 7B Instruct | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | qwen-2-5-7b-instruct | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-09 | 2026-09 |
| Knowledge Cutoff | June 2024 | April 2026 |
| Architecture | Dense Autoregressive Transformer | Near-Astra unified reasoning model |
| Parameters | 7B | 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) | Flat rate ($0.06/1M in) across full 131K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.012/1M tokens | 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) · 4 GB VRAM (4-bit Quantized) · 15 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 |
Qwen 2.5 7B Instruct vs GPT-6.1 Sol: Respostas Rápidas (FAQ)
Which is more intelligent, Qwen 2.5 7B Instruct or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 35 on the composite Intelligence Index.
Which model is faster?
Qwen 2.5 7B Instruct is faster, generating 130 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Qwen 2.5 7B Instruct is cheaper at $0.03 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen 2.5 7B Instruct has lower latency, with a time to first token of 0.29s 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 131.1K tokens.
Veredito
Choose Qwen 2.5 7B Instruct for automação corporativa, engenharia de software, agentes autônomos e pipelines de produção com alibaba, or choose GPT-6.1 Sol for automação corporativa, engenharia de software, agentes autônomos e pipelines de produção com openai.
Perguntas Frequentes (FAQ)
Which is cheaper: Qwen 2.5 7B Instruct or GPT-6.1 Sol?+
Qwen 2.5 7B Instruct costs $0.06/1M input and $0.12/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Qwen 2.5 7B Instruct or GPT-6.1 Sol?+
Qwen 2.5 7B Instruct supports 131K 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.
Fontes oficiais: Alibaba & OpenAI Official Documentation