GPT-2 vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-2 by OpenAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
Beliebige Modelle wechseln & vergleichen
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
GPT-2
Open-Weights-BasismodellGPT-2 von OpenAI ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 1K Kontextfenster, 120 Token/s Durchsatz, einem Intelligenz-Index von 12/100 und kostenlosem Open-Weights-Zugang.
$0.00 / 1M
$0.00 / 1M
1K tokens
N/A
Llama 5
Open-Weights-BasismodellLlama 5 von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 5M Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und kostenlosem Open-Weights-Zugang.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Wichtigste Unterschiede auf einen Blick
- Both models cost the same under this workload — choose on capability, not price.
- Llama 5 has a larger context window (5M vs 1K tokens).
- GPT-2 is faster at 120 tok/s vs 65 tok/s.
| Feature | GPT-2 | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 12 | 94 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1K tokens~2 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 1K | 128K |
Throughput Speed Tokens generated per second | 120 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 74% |
GPQA Diamond Graduate-level reasoning | N/A | 84% |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
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.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-2 · Strengths & Key Advantages
- 1K Kontextfenster und bis zu 1K maximale Output-Token (MMLU-Pro 15.2% · Arena Elo 750)
- Hohe Generierungsgeschwindigkeit von 120 Token/Sekunde bei nur 300 ms Erstlatenz (TTFT)
- Offene Modellgewichte (1.5B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
- Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
- Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen
Llama 5 · Strengths & Key Advantages
- 5M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 74% · GPQA Diamond 84%)
- Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
- Offene Modellgewichte (600B+ total (~40-80B )) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
- Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
- Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-2 | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.00 |
Output Tokens / Task Tokens generated per task | 24K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 20K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-2 | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-2 | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2019-02 | 2026-04 |
| Knowledge Cutoff | 2019 | January 2026 |
| Architecture | 48-layer Decoder Transformer | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 1.5B | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision, video, audio |
| Output Modalities | text | text |
GPT-2 vs Llama 5: Schnelle Antworten (FAQ)
Which is more intelligent, GPT-2 or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 12 on the composite Intelligence Index.
Which model is faster?
GPT-2 is faster, generating 120 tokens per second compared with 65 tokens per second.
Which model has lower latency?
GPT-2 has lower latency, with a time to first token of 0.30s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1K tokens.
Fazit
Choose GPT-2 for optimal für self-hosting im eigenen rechenzentrum, fine-tuning und volle datensouveränität, enterprise-rag-systeme und skalierbare api-pipelines., or choose Llama 5 for optimal für self-hosting im eigenen rechenzentrum, fine-tuning und volle datensouveränität, enterprise-rag-systeme und skalierbare api-pipelines..
Häufig gestellte Fragen (FAQ)
Which is cheaper: GPT-2 or Llama 5?+
GPT-2 costs $0/1M input and $0/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-2 or Llama 5?+
GPT-2 supports 1K tokens whereas Llama 5 supports 5M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Aion-3.0 vs. Aion-Mini: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aion-3.0 vs. Gemini 3.6 Flash: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aion-Mini vs. Laguna XS 2.1: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 32B vs. Llama 3.2 90B Vision: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 8B vs. Command R+: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 8B vs. Qwen 2.5 7B Instruct: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
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.
Offizielle Quellen: OpenAI & Meta Official Documentation