Command R7B Arabic vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Command R7B Arabic by Cohere against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Command R7B Arabic
Modello Base a Pesi ApertiCommand R7B Arabic di Cohere è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 135 token/s, indice di intelligenza di 39/
$0.07 / 1M
$0.20 / 1M
128K tokens
26%
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
- Command R7B Arabic is 38.6x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 128K tokens).
- Command R7B Arabic is faster at 135 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 26.0%).
| Feature | Command R7B Arabic | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 39 | 94 |
Provider | Cohere | OpenAI |
Input Price Cost per million input tokens | $0.07 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 128K |
Throughput Speed Tokens generated per second | 135 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 26% | 82.3% |
GPQA Diamond Graduate-level reasoning | 43% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 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.
Command R7B Arabic · Strengths & Key Advantages
- Finestra di contesto da 128K con capacità massima di output fino a 4K token (SWE-bench Verified 26% · GPQA Diamond 43%)
- Throughput elevato di 135 token/secondo con latenza al primo token (TTFT) di soli 310 ms
- Pesi aperti (7B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
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 | Command R7B Arabic | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.05 | $2.05 |
Output Tokens / Task Tokens generated per task | 20K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Command R7B Arabic | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | command-r7b-arabic | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-02 | 2026-09 |
| Knowledge Cutoff | December 2024 | April 2026 |
| Architecture | Arabic-Specialized RAG Transformer | Near-Astra unified reasoning model |
| Parameters | 7B | 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 ($0.07/1M in) across full 128K window | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | 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) | 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) · 4 GB VRAM (4-bit Quantized) · 15 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 | text, vision |
| Output Modalities | text | text |
Command R7B Arabic vs GPT-6.1 Sol: Risposte Rapide (FAQ)
Which is more intelligent, Command R7B Arabic or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 39 on the composite Intelligence Index.
Which model is faster?
Command R7B Arabic is faster, generating 135 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Command R7B Arabic is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command R7B Arabic has lower latency, with a time to first token of 0.31s compared with 1.12s.
Which model has a larger context window?
GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdetto
Choose Command R7B Arabic for ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, 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: Command R7B Arabic or GPT-6.1 Sol?+
Command R7B Arabic costs $0.07/1M input and $0.2/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Command R7B Arabic or GPT-6.1 Sol?+
Command R7B Arabic supports 128K 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: Cohere & OpenAI Official Documentation