GPT-4 vs GPT-3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-4 by OpenAI and GPT-3 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-3 holds a notable pricing advantage for token ingestion. On output generation, GPT-3 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-4 provides an expanded memory horizon with 8K tokens. In latency-sensitive production environments, GPT-3 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
Frontier PioneerOpenAI’s historic flagship foundation model displaying human-level performance on academic examinations, professional bar exams, and complex programming challenges.
$30.00 / 1M
$60.00 / 1M
8.2K tokens
21%
GPT-3
Few-Shot BreakthroughThe monumental 175-billion parameter foundation model that revealed few-shot in-context learning and ushered in the modern LLM commercial era.
$20.00 / 1M
$20.00 / 1M
2K tokens
N/A
Key Differences at a Glance
- GPT-3 is 2.0x cheaper for API usage (input + output tokens combined).
- GPT-4 has a larger context window (8K vs 2K tokens).
- GPT-3 is faster at 40 tok/s vs 35 tok/s.
| Feature | GPT-4 | GPT-3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 22 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $30.00 / 1M | $20.00 / 1M |
Output Price Cost per million output tokens | $60.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $17.25 / 1M | $9.50 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 2K tokens~3 A4 pages of text |
Max Output Tokens Per single request | 4K | 2K |
Throughput Speed Tokens generated per second | 35 tok/s | 40 tok/s |
SWE-bench Verified Real-world software engineering | 21% | N/A |
GPQA Diamond Graduate-level reasoning | 35.8% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $7.50 / 1M | $5.00 / 1M |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-4 · Strengths & Key Advantages
- Passed uniform bar exam in top 10th percentile and high school Olympiads
- First large-scale multimodal foundation model demonstrating diagram interpretation
- Superior complex instruction following and policy compliance
Considerations & Trade-offs
- High API cost ($30/$60 per 1M tokens) compared to modern optimized descendants
- Slow inference speed (35 tok/s) and limited 8K default context
GPT-3 · Strengths & Key Advantages
- Groundbreaking few-shot in-context learning ability without gradient updates
- Broad general knowledge spanning human history, literature, and basic code
- Sparked the global generative AI API ecosystem
Considerations & Trade-offs
- Context window limited to 2,048 tokens
- High inference cost ($20/1M tokens) prior to modern optimization passes
- Lacked RLHF alignment in original base form
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-4 | GPT-3 |
|---|---|---|
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 |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-4 | GPT-3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-4 | gpt-3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2023-03 | 2020-06 |
| Knowledge Cutoff | September 2021 | June 2020 |
| Architecture | Mixture-of-Experts (MoE) | Autoregressive Transformer (Davinci) |
| Parameters | Confidential (est. 1.8T MoE) | 175B |
| 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 ($20/1M in) across full 2K 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) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Prompt-based JSON extraction only |
| 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) |
| 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 |
| Output Modalities | text | text |
GPT-4 vs GPT-3: Quick Answers
Which is more intelligent, GPT-4 or GPT-3?
GPT-4 is more intelligent. It scores 38 compared with 22 on the composite Intelligence Index.
Which model is faster?
GPT-3 is faster, generating 40 tokens per second compared with 35 tokens per second.
Which model is cheaper to run?
GPT-3 is cheaper at $9.50 per 1M tokens blended, compared with $17.25 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-3 has lower latency, with a time to first token of 0.90s compared with 1.20s.
Which model has a larger context window?
GPT-4 has a larger context window, supporting 8.2K tokens compared with 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-3 if your workflow prioritizes In-context learning benchmarks, historic evaluation, and legacy generation workloads. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does GPT-4 compare to GPT-3 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-3 costs $20.00 input and $20.00 output. Overall, GPT-3 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-3?+
GPT-4 supports a context window of 8K tokens (~6k words), while GPT-3 supports 2K tokens (~2k words). Therefore, GPT-4 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-3?+
GPT-4 scores 21% on SWE-bench Verified, while GPT-3 scores competitively for software generation. For complex autonomous debugging and agentic engineering, GPT-4 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GPT-4 and GPT-3?+
GPT-4 is optimized for low-latency direct generation. In contrast, GPT-3 focuses on standard fast inference.
How does inference speed (tokens per second) compare between GPT-4 and GPT-3?+
GPT-4 generates approximately 35 tokens per second, while GPT-3 delivers 40 tokens per second. In latency-sensitive production environments, GPT-3 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-3 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-3 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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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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs