Muse Spark 1.1 vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Muse Spark 1.1 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.
Muse Spark 1.1
Agent multimodal natifde Meta propriétaire fermé multimodal agent modèle intégrant visuel chaîne de pensée et multi-agent orchestration.
$3.00 / 1M
$15.00 / 1M
1M tokens
76.5%
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
- GPT-6.1 Sol is 1.5x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol is faster at 120 tok/s vs 105 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 76.5%).
| Caractéristique | Muse Spark 1.1 | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 90 | 94 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $3.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.31 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 64K | 128K |
Throughput Speed Tokens generated per second | 105 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 76.5% | 82.3% |
GPQA Diamond Graduate-level reasoning | 83.5% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.30 / 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.
Muse Spark 1.1 · Strengths & Key Advantages
- complet omni-modal natif ingestion à travers texte, image, audio, et vidéo
- visuel chaîne de pensée raisonnement pour complexe diagrams
- 1M fenêtre de contexte avec multi-agent orchestration
Considerations & Trade-offs
- propriétaire fermé-weights modèle (unlike Llama)
- supérieur d'entreprise token tarification
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 | Muse Spark 1.1 | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.20 | $2.05 |
Output Tokens / Task Tokens generated per task | 45K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Muse Spark 1.1 | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Muse-Spark-1.1 | gpt-6-1-sol |
| API Protocol | Meta REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | January 2026 | April 2026 |
| Architecture | Omni-modal native thinking agent architecture | Near-Astra unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($3/1M in) across full 1000K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.3/1M tokens | Automatic prefix caching at $0.1/1M (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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | 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) · Video (~258–300 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 | Meta Production Safety & Alignment Verified | 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, video, audio | text, vision |
| Output Modalities | text | text |
Muse Spark 1.1 vs GPT-6.1 Sol: Réponses Rapides (FAQ)
Which is more intelligent, Muse Spark 1.1 or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 90 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 105 tokens per second.
Which model is cheaper to run?
GPT-6.1 Sol is cheaper at $1.47 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Muse Spark 1.1 has lower latency, with a time to first token of 0.60s compared with 1.12s.
Verdict
Choose Muse Spark 1.1 for d'entreprise multimodal agent systems, ui analyse, et complexe collaborative tâche exécution, or choose GPT-6.1 Sol for le meilleur valeur modèle de pointe pour professionnel et agentic travail.
Questions fréquentes
Which is cheaper: Muse Spark 1.1 or GPT-6.1 Sol?+
Muse Spark 1.1 costs $3/1M input and $15/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Muse Spark 1.1 or GPT-6.1 Sol?+
Muse Spark 1.1 supports 1M 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.
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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 & OpenAI Official Documentation