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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Live comparison: no in-depth editorial review has been published for this pair yet.
Mistral 7B
Model Open-WeightsMistral 7B dari Mistral AI adalah model AI canggih dengan jendela konteks 32K token serta harga API $0.06/1M token input dan $0.06/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$0.06 / 1M
$0.06 / 1M
32.8K tokens
N/A
GPT-6.1 Sol
Model Penalaran MendalamGPT-6.1 Sol dari OpenAI adalah model AI canggih dengan jendela konteks 1M token serta harga API $2.0/1M token input dan $10.0/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral 7B · Strengths & Key Advantages
- Arsitektur Sliding Window Attention (SWA) Transformer mutakhir dari Mistral AI dengan dukungan konteks 32K token
- Efisiensi biaya API yang kompetitif ($0.06/1M input, $0.06/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
GPT-6.1 Sol · Strengths & Key Advantages
- Arsitektur Near-Astra unified reasoning model mutakhir dari OpenAI dengan dukungan konteks 1M token
- Efisiensi biaya API yang kompetitif ($2.0/1M input, $10.0/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Choose Mistral 7B for otomasi perusahaan, pengembangan perangkat lunak, agen ai, dan alur kerja produksi berbasis mistral ai, or choose GPT-6.1 Sol for otomasi perusahaan, pengembangan perangkat lunak, agen ai, dan alur kerja produksi berbasis openai.
Pertanyaan Umum (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.
Sumber resmi: Mistral AI & OpenAI Official Documentation