GPT-4o vs GPT-4 Turbo
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-4o 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-4o holds a notable pricing advantage for token ingestion. On output generation, GPT-4o is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, GPT-4o 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-4o
Bourreau de travail omnimodalLe modèle de fondation omnimodal phare d'OpenAI intégrant nativement la voix, la vision et le texte avec une vitesse élevée et une fidélité d'appel d'outils exceptionnelle.
$2.50 / 1M
$10.00 / 1M
128K tokens
38.8%
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-4o is 3.4x cheaper for API usage (input + output tokens combined).
- GPT-4o is faster at 105 tok/s vs 55 tok/s.
- GPT-4o leads on SWE-bench coding (38.8% vs 27.5%).
| Caractéristique | GPT-4o | GPT-4 Turbo |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 41 | 40 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $2.50 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $30.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.38 / 1M | $8.50 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 16K | 4K |
Throughput Speed Tokens generated per second | 105 tok/s | 55 tok/s |
SWE-bench Verified Real-world software engineering | 38.8% | 27.5% |
GPQA Diamond Graduate-level reasoning | 53.6% | 48% |
Cached Input Price Prompt caching discount per 1M tokens | $1.25 / 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.
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.
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-4o · Strengths & Key Advantages
- natif multimodal audio, vision, et en temps réel voix intégration
- rapide inférence vitesse (105 tokens/sec) et faible latence
- fiable structuré JSON sortie et function calling
- Wide d'entreprise outil écosystème et SDK support
Considerations & Trade-offs
- fenêtre de contexte capped à 128K tokens
- SWE-bench ingénierie logicielle score inférieur que spécialisé raisonnement modèles
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-4o | GPT-4 Turbo |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.68 | $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-4o | GPT-4 Turbo |
|---|---|---|
| Official API Model ID (SDK) | gpt-4o | gpt-4-turbo |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-05 | 2023-11 |
| Knowledge Cutoff | October 2023 | December 2023 |
| Architecture | Omni-Multimodal Dense | Optimized Sparse MoE |
| Parameters | Confidential | 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 ($2.5/1M in) across full 128K window | Flat rate ($10/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $1.25/1M (no extra cache-write fee) | Automatic prefix caching at $5/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) | 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, vision, audio | text, vision |
| Output Modalities | text, audio | text |
GPT-4o vs GPT-4 Turbo: Réponses Rapides (FAQ)
Which is more intelligent, GPT-4o or GPT-4 Turbo?
GPT-4o is more intelligent. It scores 41 compared with 40 on the composite Intelligence Index.
Which model is faster?
GPT-4o is faster, generating 105 tokens per second compared with 55 tokens per second.
Which model is cheaper to run?
GPT-4o is cheaper at $2.38 per 1M tokens blended, compared with $8.50 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.65s.
Verdict
Verdict & Model Selection Recommendation: Choose GPT-4o if your priority is Omni-modal voice applications, conversational agents, and structured enterprise APIs. 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-4o compare to GPT-4 Turbo in API token pricing and cost per token?+
GPT-4o costs $2.50 per 1M input tokens and $10.00 per 1M output tokens, whereas GPT-4 Turbo costs $10.00 input and $30.00 output. Overall, GPT-4o 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-4o or GPT-4 Turbo?+
Both GPT-4o and GPT-4 Turbo offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: GPT-4o or GPT-4 Turbo?+
GPT-4o scores 38.8% on SWE-bench Verified while GPT-4 Turbo scores 27.5%. For autonomous software engineering and multi-file debugging, GPT-4o holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-4o and GPT-4 Turbo?+
GPT-4o 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-4o and GPT-4 Turbo?+
GPT-4o generates approximately 105 tokens per second, while GPT-4 Turbo delivers 55 tokens per second. In latency-sensitive production environments, GPT-4o provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-4o and GPT-4 Turbo to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-4o to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4 Turbo. 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