Llama 4 Scout vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 4 Scout by Meta against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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.
Llama 4 Scout
Roi du contexte 10Mmonde-record à long contexte ouvert modèle supporting ~3.5 millions tokens pour complet codebase et vidéo library ingestion.
$0.00 / 1M
$0.00 / 1M
3.5M tokens
N/A
GPT-6.1 Sol
Valeur proche d'Astraoffre results close à GPT-6 Astra à roughly one fifth du prix — le latest 2026 version.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Différences Clés en un Coup d’Œil
- Llama 4 Scout is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Scout has a larger context window (4M vs 1M tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 95 tok/s.
| Caractéristique | Llama 4 Scout | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 94 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 3.5M tokens~5,250 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 95 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores 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 4 Scout · Strengths & Key Advantages
- Colossal 3.5 millions token fenêtre de contexte
- Sparse MoE architecture keeps actif compute à seulement 17B
- à poids ouverts available pour local déploiement
Considerations & Trade-offs
- nécessite élevé unified mémoire à store le complet 109B weights
- Secondary raisonnement profondeur par rapport à dense raisonnement modèles
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results à roughly un fifth du coût
- Excellent codage et à long terme agent performances
- solide reliability avec far inférieur latence que Astra
- Near-phare results à roughly un fifth du prix
Considerations & Trade-offs
- Still marginally behind Astra sur le très hardest raisonnement
- Same prix as GPT-6 Sol, so le upgrade decision est easy
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Scout | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.05 |
Output Tokens / Task Tokens generated per task | 10K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Llama 4 Scout | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Scout | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | January 2025 | April 2026 |
| Architecture | Extreme-context sparse MoE multimodal transformer (17B active / 109B total) | Near-Astra unified reasoning model |
| Parameters | 17B (109B total MoE) | 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.1/1M (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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 11 GB VRAM (4-bit Quantized) · 37 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) | OpenAI Preparedness Framework Audited & System Card Published |
| 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, vision | text, vision |
| Output Modalities | text | text |
Llama 4 Scout vs GPT-6.1 Sol: Réponses Rapides (FAQ)
Which is more intelligent, Llama 4 Scout or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 76 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 95 tokens per second.
Which model is cheaper to run?
Llama 4 Scout is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 4 Scout has lower latency, with a time to first token of 0.65s compared with 1.12s.
Which model has a larger context window?
Llama 4 Scout has a larger context window, supporting 3.5M tokens compared with 1M tokens.
Verdict
Choose Llama 4 Scout for whole-dépôt code analyse, multi-hour vidéo compréhension, et juridique book ingestion, or choose GPT-6.1 Sol for le meilleur valeur modèle de pointe pour professionnel et agentic travail.
Questions fréquentes
Which is cheaper: Llama 4 Scout or GPT-6.1 Sol?+
Llama 4 Scout costs $0/1M input and $0/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Llama 4 Scout or GPT-6.1 Sol?+
Llama 4 Scout supports 3.5M tokens whereas GPT-6.1 Sol supports 1M 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, Pricing & Speed ComparisonComparer
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed ComparisonComparer
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed ComparisonComparer
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed ComparisonComparer
- Aya Expanse 8B vs Command R+: Benchmarks, Pricing & Speed ComparisonComparer
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Benchmarks, Pricing & Speed ComparisonComparer
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Meta & OpenAI Official Documentation