Mixtral 8x7B vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Mixtral 8x7B by Mistral AI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
Mixtral 8x7B
Modelo Base de Pesos AbertosO Mixtral 8x7B da Mistral AI é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 33K, velocidade de 130 tokens/s, índice de inteligência de 37/100 e
$0.20 / 1M
$0.20 / 1M
32.8K tokens
N/A
GPT-6.1 Sol
Modelo Principal de FronteiraO GPT-6.1 Sol da OpenAI é um modelo de IA projetado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, oferecendo janela de contexto de 1M, velocidade de 120 tokens/s, índice de inteligência de 94/100 e tar
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Principais Diferenças em Resumo
- Mixtral 8x7B is 21.9x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 33K tokens).
- Mixtral 8x7B is faster at 130 tok/s vs 120 tok/s.
| Feature | Mixtral 8x7B | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 37 | 94 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.20 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 130 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | 38% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.10 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkEvaluations ongoing
Pontuações 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.
Mixtral 8x7B · Strengths & Key Advantages
- Janela de contexto de 33K com limite máximo de saída de 4K tokens (GPQA Diamond 38% · MMLU-Pro 66.5%)
- Taxa de transferência rápida de 130 tokens/segundo com latência inicial (TTFT) de 380 ms
- Pesos abertos (46.7B (12.9B )) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
GPT-6.1 Sol · Strengths & Key Advantages
- Janela de contexto de 1M com limite máximo de saída de 128K tokens (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
- Taxa de transferência rápida de 120 tokens/segundo com latência inicial (TTFT) de 1120 ms
- Economia de API competitiva ($2/$10 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mixtral 8x7B | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $2.05 |
Output Tokens / Task Tokens generated per task | 24K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Mixtral 8x7B | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | mixtral-8x7b-latest | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2023-12 | 2026-09 |
| Knowledge Cutoff | Late 2023 | April 2026 |
| Architecture | Sparse Mixture of Experts (MoE, 2 active of 8) | Near-Astra unified reasoning model |
| Parameters | 46.7B (12.9B active) | 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 ($0.2/1M in) across full 33K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M 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) · 29 GB VRAM (4-bit Quantized) · 100 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 |
Mixtral 8x7B vs GPT-6.1 Sol: Respostas Rápidas (FAQ)
Which is more intelligent, Mixtral 8x7B or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 37 on the composite Intelligence Index.
Which model is faster?
Mixtral 8x7B is faster, generating 130 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Mixtral 8x7B is cheaper at $0.10 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Mixtral 8x7B has lower latency, with a time to first token of 0.38s 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 32.8K tokens.
Veredito
Choose Mixtral 8x7B for indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas rag corporativas e integração escalável via api., or choose GPT-6.1 Sol for indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas rag corporativas e integração escalável via api..
Perguntas Frequentes (FAQ)
Which is cheaper: Mixtral 8x7B or GPT-6.1 Sol?+
Mixtral 8x7B costs $0.2/1M input and $0.2/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Mixtral 8x7B or GPT-6.1 Sol?+
Mixtral 8x7B supports 33K 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: Mistral AI & OpenAI Official Documentation