Mistral 7B vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Mistral 7B by Mistral AI against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Mistral 7B
超高速・低遅延レスポンスMistral AI の Mistral 7B は、超低遅延のリアルタイム対話、高速ルーティング、大量リクエスト処理向けに設計されたAIモデルです。33K のコンテキストウィンドウ、毎秒 200 トークンの生成速度、知能指数 32/100（1Mトークンあたり入力$0.06・出力$0.06）を実現しています。
$0.06 / 1M
$0.06 / 1M
32.8K tokens
N/A
GPT-6.1 Sol
フラッグシップ最先端モデルOpenAI の GPT-6.1 Sol は、最先端のエンタープライズ推論、ソフトウェア工学、ミッションクリティカルな本番運用向けに設計されたAIモデルです。1M のコンテキストウィンドウ、毎秒 120 トークンの生成速度、知能指数 94/100（1Mトークンあたり入力$2・出力$10）を実現しています。
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
ひと目でわかる主な違い
- Mistral 7B is 73.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 33K tokens).
- Mistral 7B is faster at 200 tok/s vs 120 tok/s.
| Feature | Mistral 7B | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 32 | 94 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.06 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.06 / 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 | 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 | 200 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral 7B · Strengths & Key Advantages
- 33K のコンテキストウィンドウと最大 4K トークンの出力容量（MMLU-Pro 56% · Arena Elo 1120）
- 毎秒 200 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 240ms の低遅延
- オープンウェイトモデル (7.3B)として vLLM・SGLang・Ollama を用いた自社GPUでのローカル運用が可能
Considerations & Trade-offs
- 非量子化のフル精度でローカル推論を行うには十分なGPU VRAM容量が必要です
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
GPT-6.1 Sol · Strengths & Key Advantages
- 1M のコンテキストウィンドウと最大 128K トークンの出力容量（SWE-bench Verified 82.3% · GPQA Diamond 80.8%）
- 毎秒 120 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 1120ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$2・出力$10）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Considerations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 大量のトークンを処理する本番環境では、プロンプトキャッシュや Batch API の併用によるコスト最適化が推奨されます
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral 7B | 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 |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral 7B | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | mistral-7b-latest | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2023-09 | 2026-09 |
| Knowledge Cutoff | Mid 2023 | April 2026 |
| Architecture | Sliding Window Attention (SWA) Transformer | Near-Astra unified reasoning model |
| Parameters | 7.3B | 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 33K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.015/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | 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) · 5 GB VRAM (4-bit Quantized) · 16 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 |
Mistral 7B vs GPT-6.1 Sol: よくある質問 (FAQ)
Which is more intelligent, Mistral 7B or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 32 on the composite Intelligence Index.
Which model is faster?
Mistral 7B is faster, generating 200 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Mistral 7B 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?
Mistral 7B has lower latency, with a time to first token of 0.24s 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.
総合評価
Choose Mistral 7B for 超低遅延のリアルタイム対話、高速ルーティング、大量リクエスト処理、エンタープライズrag基盤、スケーラブルな本番api統合に最適。, or choose GPT-6.1 Sol for 最先端のエンタープライズ推論、ソフトウェア工学、ミッションクリティカルな本番運用、エンタープライズrag基盤、スケーラブルな本番api統合に最適。.
よくある質問 (FAQ)
Which is cheaper: Mistral 7B or GPT-6.1 Sol?+
Mistral 7B costs $0.06/1M input and $0.06/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Mistral 7B or GPT-6.1 Sol?+
Mistral 7B 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.
公的ソース: Mistral AI & OpenAI Official Documentation