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.
Cambia e Confronta Qualsiasi Modello
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Muse Spark 1.1
IA Multimodale NativaMuse Spark 1.1 di Meta è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 1M, velocità di 105 token/s, indice di intelligenza di 90/100 e ta
$3.00 / 1M
$15.00 / 1M
1M tokens
76.5%
GPT-6.1 Sol
Modello Ammiraglia di FrontieraGPT-6.1 Sol di OpenAI è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1M, velocità di 120 token/s, indice di intelligenza di 94/100
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Differenze Chiave a Colpo d’Occhio
- 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%).
| Feature | 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Muse Spark 1.1 · Strengths & Key Advantages
- Finestra di contesto da 1M con capacità massima di output fino a 64K token (SWE-bench Verified 76.5% · GPQA Diamond 83.5%)
- Throughput elevato di 105 token/secondo con latenza al primo token (TTFT) di soli 600 ms
- Economia API competitiva ($3/$15 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
GPT-6.1 Sol · Strengths & Key Advantages
- Finestra di contesto da 1M con capacità massima di output fino a 128K token (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
- Throughput elevato di 120 token/secondo con latenza al primo token (TTFT) di soli 1120 ms
- Economia API competitiva ($2/$10 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
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 |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: Risposte Rapide (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.
Verdetto
Choose Muse Spark 1.1 for ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi rag aziendali e integrazioni api scalabili., or choose GPT-6.1 Sol for ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi rag aziendali e integrazioni api scalabili..
Domande Frequenti (FAQ)
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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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.
Fonti ufficiali: Meta & OpenAI Official Documentation