Llama 3 (8B) vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3 (8B) by Meta against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
Llama 3 (8B)
Puissance en périphérieHyper-efficient compact modèle entraîné sur sur 15 billions tokens, punchy enough à outperform earlier 70B modèles sur périphérie matériel et unique grand public GPUs.
$0.05 / 1M
$0.10 / 1M
8.2K tokens
12%
Gemini 4 Argon
Roi multimodal avec sortie 1MGoogle de DeepMind phare de pointe raisonnement moteur intégrant 1-millions-token sortie limits, autonome cybersecurity defense, et en temps réel audio/vision ingestion.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Différences Clés en un Coup d’Œil
- Llama 3 (8B) is 66.1x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 8K tokens).
- Llama 3 (8B) is faster at 210 tok/s vs 115 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 12.0%).
| Caractéristique | Llama 3 (8B) | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 34 | 97 |
Provider | Meta | |
Input Price Cost per million input tokens | $0.05 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.10 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 1000K |
Throughput Speed Tokens generated per second | 210 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 12% | 81.9% |
GPQA Diamond Graduate-level reasoning | 34.2% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 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.
Llama 3 (8B) · Strengths & Key Advantages
- fonctionne effortlessly sur un unique grand public GPU (RTX 4090 / Mac M-series)
- entraîné sur 15T tokens avec 128K Tiktoken vocabulary pour supérieur token density
- le plus rapide token débit (210 tok/s) dans le à poids ouverts classe
Considerations & Trade-offs
- Original Llama 3 version had un 8K fenêtre de contexte before 3.1 upgraded à 128K
- Prone à occasional logical brevity sur complexe scientifique prompts
Gemini 4 Argon · Strengths & Key Advantages
- Groundbreaking 1,000,000 token maximum sortie génération capacité via long Decode Continuation
- 1M-token entrée window avec 99.7% GraphWalks recall (84.2% dans le 256K-1M range)
- natif traitement de complet audio, vidéo streams, et visuel données (91.7% sur LVBench)
- Half le token prix de d'Anthropic Opus 5.5 et #1 sur Chatbot Arena (1525 Elo)
Considerations & Trade-offs
- massif token outputs peut incur élevé absolute coûts if unconstrained (~62K sortie tokens par index tâche)
- $2/$10 tarification est un introductory 50% discount; standard rates revert à $4/$20 once le promotion ends
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3 (8B) | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $2.45 |
Output Tokens / Task Tokens generated per task | 24K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Llama 3 (8B) | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3-(8B) | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-04 | 2026-09 |
| Knowledge Cutoff | March 2024 | June 2026 |
| Architecture | Dense GQA Transformer with 128K Tokenizer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 8B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.05/1M in) across full 8K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.0125/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision, audio, video |
| Output Modalities | text | text |
Llama 3 (8B) vs Gemini 4 Argon: Réponses Rapides (FAQ)
Which is more intelligent, Llama 3 (8B) or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 34 on the composite Intelligence Index.
Which model is faster?
Llama 3 (8B) is faster, generating 210 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Llama 3 (8B) is cheaper at $0.03 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3 (8B) has lower latency, with a time to first token of 0.22s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 8.2K tokens.
Verdict
Choose Llama 3 (8B) for local périphérie computing, offline laptop inférence, robotics, et faible-latence api routers, or choose Gemini 4 Argon for whole-dépôt génération de code, complet-book synthèse, vidéo compréhension, et multi-hour audio traitement.
Questions fréquentes
Which is cheaper: Llama 3 (8B) or Gemini 4 Argon?+
Llama 3 (8B) costs $0.05/1M input and $0.1/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Llama 3 (8B) or Gemini 4 Argon?+
Llama 3 (8B) supports 8K tokens whereas Gemini 4 Argon supports 1M tokens.
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: Meta & Google Official Documentation