GPT-4 vs GPT-4 Turbo
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-4 by OpenAI and GPT-4 Turbo by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GPT-4 Turbo holds a notable pricing advantage for token ingestion. On output generation, GPT-4 Turbo is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-4 Turbo provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, GPT-4 Turbo leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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GPT-4
Pionnier de pointed'OpenAI historic phare modèle de fondation displaying human-level performances sur académique examinations, professionnel bar exams, et complexe programmation challenges.
$30.00 / 1M
$60.00 / 1M
8.2K tokens
21%
GPT-4 Turbo
Bourreau de production 128KMajor architectural upgrade intégrant un 128K fenêtre de contexte, natif vision intégration, reproducible JSON mode, et significantly reduced API tarification.
$10.00 / 1M
$30.00 / 1M
128K tokens
27.5%
Différences Clés en un Coup d’Œil
- GPT-4 Turbo is 2.2x cheaper for API usage (input + output tokens combined).
- GPT-4 Turbo has a larger context window (128K vs 8K tokens).
- GPT-4 Turbo is faster at 55 tok/s vs 35 tok/s.
- GPT-4 Turbo leads on SWE-bench coding (27.5% vs 21.0%).
| Caractéristique | GPT-4 | GPT-4 Turbo |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 40 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $30.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $60.00 / 1M | $30.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $17.25 / 1M | $8.50 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 35 tok/s | 55 tok/s |
SWE-bench Verified Real-world software engineering | 21% | 27.5% |
GPQA Diamond Graduate-level reasoning | 35.8% | 48% |
Cached Input Price Prompt caching discount per 1M tokens | $7.50 / 1M | $5.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-4 · Strengths & Key Advantages
- Passed uniform bar exam dans haut de gamme 10th percentile et élevé school Olympiads
- premier grand-échelle multimodal modèle de fondation demonstrating diagram interpretation
- supérieur complexe instruction following et policy conformité
Considerations & Trade-offs
- élevé API coût ($30/$60 par 1M tokens) par rapport à moderne optimized descendants
- Slow inférence vitesse (35 tok/s) et limité 8K default contexte
GPT-4 Turbo · Strengths & Key Advantages
- 128,000 token fenêtre de contexte capable d'ingesting entire books et documentation suites
- Sharp 3x prix reduction d'original GPT-4
- fiable JSON object mode et parallel appel d'outils
Considerations & Trade-offs
- Max sortie capped à 4,096 tokens
- Outpaced dans débit par subsequent GPT-4o omni releases
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-4 | GPT-4 Turbo |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.20 |
Output Tokens / Task Tokens generated per task | 24K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | GPT-4 | GPT-4 Turbo |
|---|---|---|
| Official API Model ID (SDK) | gpt-4 | gpt-4-turbo |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2023-03 | 2023-11 |
| Knowledge Cutoff | September 2021 | December 2023 |
| Architecture | Mixture-of-Experts (MoE) | Optimized Sparse MoE |
| Parameters | Confidential (est. 1.8T MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($30/1M in) across full 8K window | Flat rate ($10/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $7.5/1M (no extra cache-write fee) | Automatic prefix caching at $5/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | Cloud-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 Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
GPT-4 vs GPT-4 Turbo: Réponses Rapides (FAQ)
Which is more intelligent, GPT-4 or GPT-4 Turbo?
GPT-4 Turbo is more intelligent. It scores 40 compared with 38 on the composite Intelligence Index.
Which model is faster?
GPT-4 Turbo is faster, generating 55 tokens per second compared with 35 tokens per second.
Which model is cheaper to run?
GPT-4 Turbo is cheaper at $8.50 per 1M tokens blended, compared with $17.25 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4 Turbo has lower latency, with a time to first token of 0.65s compared with 1.20s.
Which model has a larger context window?
GPT-4 Turbo has a larger context window, supporting 128K tokens compared with 8.2K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose GPT-4 if your priority is Complex multi-step legal analysis, nuanced philosophical debate, and academic benchmark research. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4 Turbo if your workflow prioritizes Enterprise document review, production agent orchestration, and long-form visual analysis. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does GPT-4 compare to GPT-4 Turbo in API token pricing and cost per token?+
GPT-4 costs $30.00 per 1M input tokens and $60.00 per 1M output tokens, whereas GPT-4 Turbo costs $10.00 input and $30.00 output. Overall, GPT-4 Turbo is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-4 or GPT-4 Turbo?+
GPT-4 supports a context window of 8K tokens (~6k words), while GPT-4 Turbo supports 128K tokens (~96k words). Therefore, GPT-4 Turbo can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-4 or GPT-4 Turbo?+
GPT-4 scores 21% on SWE-bench Verified while GPT-4 Turbo scores 27.5%. For autonomous software engineering and multi-file debugging, GPT-4 Turbo holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-4 and GPT-4 Turbo?+
GPT-4 is optimized for low-latency direct generation. In contrast, GPT-4 Turbo focuses on standard fast inference.
How does inference speed (tokens per second) compare between GPT-4 and GPT-4 Turbo?+
GPT-4 generates approximately 35 tokens per second, while GPT-4 Turbo delivers 55 tokens per second. In latency-sensitive production environments, GPT-4 Turbo provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-4 and GPT-4 Turbo to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-4 Turbo to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs