Head-to-Head Model Comparison

Mistral 7B vs Llama 2 (70B)

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Mistral 7B vs Llama 2 (70B): Benchmarks, Pricing & Speed Comparison

Mistral 7B dari Mistral AI dan Llama 2 (70B) dari Meta adalah dua model AI unggulan. Mistral 7B menawarkan jendela konteks 32.8K token dengan harga $0.06/$0.06 per 1 juta token, sedangkan Llama 2 (70B) menyediakan konteks 4.1K token seharga $0.40/$0.80 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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Mistral 7B

Inferensi Super Cepat

Mistral 7B dari Mistral AI adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 33K, kecepatan 200 token/detik, indeks kecerdasan 32/100, serta tari

Intelligence Index32 / 100
Input Token:

$0.06 / 1M

Output Token:

$0.06 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

Meta

Llama 2 (70B)

Model Dasar Bobot Terbuka

Llama 2 (70B) dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 4K, kecepatan 55 token/detik, indeks kecerdasan 31/100, serta tarif $0.4/$0.8

Intelligence Index31 / 100
Input Token:

$0.40 / 1M

Output Token:

$0.80 / 1M

Context Window:

4.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

  • Mistral 7B is 8.8x cheaper for API usage (input + output tokens combined).
  • Mistral 7B has a larger context window (33K vs 4K tokens).
  • Mistral 7B is faster at 200 tok/s vs 55 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMistral 7BLlama 2 (70B)
Intelligence Index
Composite reasoning & knowledge score (0-100)
3231
Provider
Mistral AIMeta
Input Price
Cost per million input tokens
$0.06 / 1M$0.40 / 1M
Output Price
Cost per million output tokens
$0.06 / 1M$0.80 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.03 / 1M$0.23 / 1M
Context Window
Maximum input + output tokens
32.8K tokens~49 A4 pages of text4.1K tokens~6 A4 pages of text
Max Output Tokens
Per single request
4K4K
Throughput Speed
Tokens generated per second
200 tok/s55 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
Cached Input Price
Prompt caching discount per 1M tokens
$0.01 / 1M$0.10 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Mistral 7B
8.8x cheaper monthly runtime
Speed
Mistral 7B
+264% faster generation
Context
Mistral 7B
32.8K tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

Mistral 7BLlama 2 (70B)
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

56%vs56.8%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1120ptsvs1105pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

200 tok/s vs55 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Mistral 7B0.24s
Llama 2 (70B)0.60s
Est. End-to-End Response (500 tokens)TTFT + generation time
Mistral 7B2.74s
Llama 2 (70B)9.69s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Mistral 7B Monthly API Bill
$0.17 / month
$0.06/1M in · $0.06/1M out
Llama 2 (70B) Monthly API Bill
$1.47 / month
$0.4/1M in · $0.8/1M out
Mistral 7B saves approximately $1.30 / month (89%) under this workload.8.8x Savings

Mistral 7B · Strengths & Key Advantages

  • Jendela konteks 33K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 56% · Arena Elo 1120)
  • Kecepatan generasi tinggi 200 token/detik dengan latensi token pertama (TTFT) hanya 240 ms
  • Bobot terbuka (7.3B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Llama 2 (70B) · Strengths & Key Advantages

  • Jendela konteks 4K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 56.8% · Arena Elo 1105)
  • Kecepatan generasi tinggi 55 token/detik dengan latensi token pertama (TTFT) hanya 600 ms
  • Bobot terbuka (70B (also 7B, 13B)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricMistral 7BLlama 2 (70B)
Cost per Task
Estimated USD to finish one task
$0.20$0.20
Output Tokens / Task
Tokens generated per task
24K24K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureMistral 7BLlama 2 (70B)
Official API Model ID (SDK)mistral-7b-latestmeta-llama/Llama-2-(70B)
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2023-092023-07
Knowledge CutoffMid 2023September 2022
ArchitectureSliding Window Attention (SWA) TransformerGrouped-Query Attention (GQA) Transformer
Parameters7.3B70B (also 7B, 13B)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.06/1M in) across full 33K windowFlat rate ($0.4/1M in) across full 4K window
Prompt Cache Policy & TTLCached input at $0.015/1M tokensCached input at $0.1/1M tokens
Computer Use / GUI AgentText-only completion (No GUI action loop)Text-only completion (No GUI action loop)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyPrompt-based JSON extraction only
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 16 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Mistral 7B vs Llama 2 (70B): Jawaban Cepat (FAQ)

Which is more intelligent, Mistral 7B or Llama 2 (70B)?

Mistral 7B is more intelligent. It scores 32 compared with 31 on the composite Intelligence Index.

Which model is faster?

Mistral 7B is faster, generating 200 tokens per second compared with 55 tokens per second.

Which model is cheaper to run?

Mistral 7B is cheaper at $0.03 per 1M tokens blended, compared with $0.23 (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 0.60s.

Which model has a larger context window?

Mistral 7B has a larger context window, supporting 32.8K tokens compared with 4.1K tokens.

Kesimpulan

Baik Mistral 7B (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 2 (70B) (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Mistral 7B unggul dalam penalaran dan pemrograman, Mistral 7B memberikan kecepatan generasi token lebih tinggi, dan Mistral 7B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Mistral 7B atau Llama 2 (70B)?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Mistral 7B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Mistral 7B vs Llama 2 (70B)?+

Mistral 7B mengenakan biaya $0.06 / $0.06 per 1M token input/output, sedangkan Llama 2 (70B) sebesar $0.40 / $0.80. Secara keseluruhan, Mistral 7B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Mistral 7B mendukung konteks 32.8K token dibandingkan 4.1K token pada Llama 2 (70B), sementara Mistral 7B unggul dalam kecepatan output token per detik.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

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: Dokumentasi API resmi Mistral AI & Meta serta evaluasi benchmark independen (2026).