Head-to-Head Model Comparison

GPT-3.5 Turbo vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale
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OpenAIAnthropicfastflagship

Compare GPT-3.5 Turbo by OpenAI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.

Comparaison Face-à-Face (VS)

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OpenAI

GPT-3.5 Turbo

Révolution ChatGPT

le original moteur behind le mondial ChatGPT phenomenon. Highly optimisé pour conversationnel dialogue, rapide response, et coût-effective API déploiement.

Intelligence Index30 / 100
Input Token:

$0.50 / 1M

Output Token:

$1.50 / 1M

Context Window:

16.4K tokens

SWE-bench Verified:

12.5%

Anthropic

Claude Opus 5.5

Titan d'intelligence de pointe

d'Anthropic pinnacle modèle de pointe conçu pour à long terme agentic ingénierie logicielle, dense multimodal document synthèse, et autonome recherche flux de travail.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Différences Clés en un Coup d’Œil

  • GPT-3.5 Turbo is 9.9x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 16K tokens).
  • GPT-3.5 Turbo is faster at 90 tok/s vs 78 tok/s.
  • Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 12.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaractéristiqueGPT-3.5 TurboClaude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
3094
Provider
OpenAIAnthropic
Input Price
Cost per million input tokens
$0.50 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$1.50 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.42 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
16.4K tokens~25 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
4K128K
Throughput Speed
Tokens generated per second
90 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
12.5%84.6%
GPQA Diamond
Graduate-level reasoning
28%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.25 / 1M$0.40 / 1M

Qui gagne où ?

At-a-glance decision framework
Code
Claude Opus 5.5
+72.1% SWE-bench lead
Coût
GPT-3.5 Turbo
9.9x cheaper monthly runtime
Vitesse
GPT-3.5 Turbo
+15% faster generation
Contexte
Claude Opus 5.5
1M tokens window

Scores de Benchmarks Vérifiés

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

GPT-3.5 TurboClaude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

12.5%vs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

28%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

56.8%vs91.5%
LMSYS Chatbot Arena Elo

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

1115ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs66.4%
SciCode (Scientific Computing)

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

N/Avs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs83.1%
AIME (Advanced Math Competition)

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

N/Avs95.4%
Long-Context Reasoning

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

N/Avs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs78 tok/s

Latence et Temps de Réponse

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
GPT-3.5 Turbo0.45s
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-3.5 Turbo6.01s
Claude Opus 5.57.81s

Simulateur Interactif de Coût de Tokens

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)
GPT-3.5 Turbo Monthly API Bill
$2.48 / month
$0.5/1M in · $1.5/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
GPT-3.5 Turbo saves approximately $22.13 / month (90%) under this workload.9.9x Savings

GPT-3.5 Turbo · Strengths & Key Advantages

  • Accessible, fiable conversationnel assistant qui popularized generative AI globally
  • 10x coût reduction par rapport à legacy GPT-3 Davinci
  • rapide inférence et broad développeur intégration
Considerations & Trade-offs
  • manque de vision et natif multimodal compréhension
  • inférieur raisonnement et codage benchmark scores par rapport à GPT-4 classe modèles

Claude Opus 5.5 · Strengths & Key Advantages

  • inégalé autonome multi-étapes codage agentique et refactorisation
  • Flawless multidocument raisonnement sur 1M token contexte avec no long-prompt surcharge
  • le plus élevé Artificial analyse intelligence Index ever measured (58/100), leading SWE-bench Pro à 89.9%
  • Precise outil exécution avec robust error-recovery boucles
Considerations & Trade-offs
  • Premium sortie tarification ($20.00/M) plus heavy raisonnement-token usage (~119K sortie tokens par index tâche) keeps long agentic fonctionne expensive
  • long pensée times à maximum effort peut push un unique hard agentic tâche toward ~$13

Token Use & Task Economics

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

MetricGPT-3.5 TurboClaude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.20$3.85
Output Tokens / Task
Tokens generated per task
24K68K
Reasoning Tokens / Task
Thinking tokens burned per task
0K42K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

CaractéristiqueGPT-3.5 TurboClaude Opus 5.5
Official API Model ID (SDK)gpt-3-5-turboclaude-opus-5-5-20260901
API ProtocolOpenAI Responses API & Chat Completions v1Anthropic Messages API + AWS Bedrock + Vertex AI
Release Date2022-112026-09
Knowledge CutoffSeptember 2021June 2026
ArchitectureTransformer with RLHF & PPOExtended Autonomous Hybrid Reasoning Transformer
ParametersConfidential (est. 20B MoE / 175B base)Confidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 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 ($0.5/1M in) across full 16K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix caching at $0.25/1M (no extra cache-write fee)$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 & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Custom 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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedAnthropic 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

GPT-3.5 Turbo vs Claude Opus 5.5: Réponses Rapides (FAQ)

Which is more intelligent, GPT-3.5 Turbo or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 30 on the composite Intelligence Index.

Which model is faster?

GPT-3.5 Turbo is faster, generating 90 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

GPT-3.5 Turbo is cheaper at $0.42 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-3.5 Turbo has lower latency, with a time to first token of 0.45s 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 16.4K tokens.

Verdict

Choose GPT-3.5 Turbo for quotidien conversationnel chatbots, client support routage, et élevé-volume texte résumé, or choose Claude Opus 5.5 for grand-échelle logiciel development agents, d'entreprise codebases, et mission critique scientifique synthèse.

Questions fréquentes

Which is cheaper: GPT-3.5 Turbo or Claude Opus 5.5?+

GPT-3.5 Turbo costs $0.5/1M input and $1.5/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.

Which model has a larger context window: GPT-3.5 Turbo or Claude Opus 5.5?+

GPT-3.5 Turbo supports 16K 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.

Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.

Sources: OpenAI & Anthropic Official Documentation