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Head-to-Head Model Comparison

Mistral Saba vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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Mistral AIAnthropicflagshipflagship

Compare Mistral Saba by Mistral AI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.

1:1 모델 맞대결 비교 (VS)

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Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:
Mistral AI

Mistral Saba

글로벌 다국어 특화 모델

Mistral AI의 Mistral Saba 모델은 정교한 다국어 번역, 글로벌 비즈니스 커뮤니케이션 및 교차 언어 분석에 최적화된 AI 모델로, 33K 컨텍스트 윈도우, 초당 80 토큰의 생성 속도, 종합 지능 지수 40/100점(1M 토큰당 입력 $0.2 / 출력 $0.6)을 제공합니다.

Intelligence Index40 / 100
Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

38%

Anthropic

Claude Opus 5.5

플래그십 프론티어 모델

Anthropic의 Claude Opus 5.5 모델은 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 78 토큰의 생성 속도, 종합 지능 지수 94/100점(1M 토큰당 입력 $4 / 출력 $20)을 제공합니다.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

한눈에 보는 핵심 차이점

  • Mistral Saba is 26.9x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 33K tokens).
  • Mistral Saba is faster at 80 tok/s vs 78 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 38.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMistral SabaClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
4094
Provider
Mistral AIAnthropic
Input Price
Cost per million input tokens
$0.20 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$0.60 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.14 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
32.8K tokens~49 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
80 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
38%84.6%
GPQA Diamond
Graduate-level reasoning
46.5%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.05 / 1M$0.40 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Claude Opus 5.5
+46.6% SWE-bench lead
Cost
Mistral Saba
26.9x cheaper monthly runtime
Speed
Mistral Saba
+3% faster generation
Context
Claude Opus 5.5
1M tokens window

검증된 벤치마크 점수 비교

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

Mistral SabaClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

38%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

46.5%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

65.8%vs91.5%
LMSYS Chatbot Arena Elo

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

1260ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

5.2%vs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

24.5%vs66.4%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

24%vs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

41%vs83.1%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

48%vs95.4%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

59%vs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs78 tok/s

지연 시간(Latency) 및 응답 속도

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 Saba0.46s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Mistral Saba6.71s
Claude Opus 5.57.81s

실시간 API 토큰 비용 시뮬레이터

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 Saba Monthly API Bill
$0.92 / month
$0.2/1M in · $0.6/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Mistral Saba saves approximately $23.69 / month (96%) under this workload.26.9x Savings

Mistral Saba · Strengths & Key Advantages

  • 33K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (SWE-bench Verified 38% · GPQA Diamond 46.5%)
  • 초당 80 토큰의 빠른 처리 속도와 460ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $0.2 / 출력 $0.6)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
  • 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Claude Opus 5.5 · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
  • 초당 78 토큰의 빠른 처리 속도와 1400ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $4 / 출력 $20)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
  • 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

Token Use & Task Economics

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

MetricMistral SabaClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.18$3.85
Output Tokens / Task
Tokens generated per task
24K68K
Reasoning Tokens / Task
Thinking tokens burned per task
0K42K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureMistral SabaClaude Opus 5.5
Official API Model ID (SDK)mistral-saba-latestclaude-opus-5-5-20260901
API ProtocolMistral AI REST API (OpenAI-Compatible)Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2025-022026-09
Knowledge CutoffJanuary 2025June 2026
ArchitectureMultilingual Dense TransformerExtended Autonomous Hybrid Reasoning Transformer
Parameters24BConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.2/1M in) across full 33K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.05/1M tokens$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationCustom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationMistral AI Production Safety & Alignment VerifiedAnthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision
Output Modalitiestexttext

Mistral Saba vs Claude Opus 5.5: 핵심 요약 문답 (FAQ)

Which is more intelligent, Mistral Saba or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 40 on the composite Intelligence Index.

Which model is faster?

Mistral Saba is faster, generating 80 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Mistral Saba is cheaper at $0.14 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Saba has lower latency, with a time to first token of 0.46s compared with 1.40s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 32.8K tokens.

종합 평가

Choose Mistral Saba for 정교한 다국어 번역, 글로벌 비즈니스 커뮤니케이션 및 교차 언어 분석, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Claude Opus 5.5 for 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..

자주 묻는 질문 (FAQ)

Which is cheaper: Mistral Saba or Claude Opus 5.5?+

Mistral Saba costs $0.2/1M input and $0.6/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.

Which model has a larger context window: Mistral Saba or Claude Opus 5.5?+

Mistral Saba supports 33K tokens whereas Claude Opus 5.5 supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

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

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

공식 출처: Mistral AI & Anthropic Official Documentation