Nemotron Mini 4B Instruct vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Nemotron Mini 4B Instruct by NVIDIA against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Nemotron Mini 4B Instruct
Tiny NemotronUltra-compact 4B instruct model optimized by NVIDIA for on-device roleplay, RAG assistance, and PC edge deployments.
$0.05 / 1M
$0.15 / 1M
128K tokens
22%
GPT-6.1 Sol
Near-Astra ValueDelivers results close to GPT-6 Astra at roughly one fifth of the price — the latest 2026 release.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Key Differences at a Glance
- Nemotron Mini 4B Instruct is 50.6x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol has a larger context window (1M vs 128K tokens).
- Nemotron Mini 4B Instruct is faster at 160 tok/s vs 120 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 22.0%).
| Feature | Nemotron Mini 4B Instruct | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 35 | 94 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.05 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.15 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.04 / 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 | 160 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 22% | 82.3% |
GPQA Diamond Graduate-level reasoning | 38% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.10 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron Mini 4B Instruct · Strengths & Key Advantages
- Ultra-lightweight 4B footprint running smoothly on consumer RTX GPUs and laptops
- Engineered by NVIDIA with TensorRT-LLM acceleration for extreme throughput
- Full 128K context window with permissive open weights
Considerations & Trade-offs
- Limited reasoning depth on complex mathematical and scientific tasks
- Text-only without native multimodal processing
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results at roughly a fifth of the cost
- Excellent coding and long-horizon agent performance
- Strong reliability with far lower latency than Astra
- Near-flagship results at roughly a fifth of the price
Considerations & Trade-offs
- Still marginally behind Astra on the very hardest reasoning
- Same price as GPT-6 Sol, so the upgrade decision is easy
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron Mini 4B Instruct | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.05 |
Output Tokens / Task Tokens generated per task | 15K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron Mini 4B Instruct | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | nemotron-mini-4b | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-08 | 2026-09 |
| Knowledge Cutoff | June 2024 | April 2026 |
| Architecture | Pruned and Distilled Compact Transformer | Near-Astra unified reasoning model |
| Parameters | 4B | 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 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) · 2 GB VRAM (4-bit Quantized) · 9 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 |
Nemotron Mini 4B Instruct vs GPT-6.1 Sol: Quick Answers
Which is more intelligent, Nemotron Mini 4B Instruct or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 35 on the composite Intelligence Index.
Which model is faster?
Nemotron Mini 4B Instruct is faster, generating 160 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Nemotron Mini 4B Instruct is cheaper at $0.04 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron Mini 4B Instruct has lower latency, with a time to first token of 0.25s 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.
Verdict
Choose Nemotron Mini 4B Instruct for local desktop ai companions, offline edge pcs, and high-speed data extraction, or choose GPT-6.1 Sol for the best value frontier model for professional and agentic work.
Frequently asked questions
Which is cheaper: Nemotron Mini 4B Instruct or GPT-6.1 Sol?+
Nemotron Mini 4B Instruct costs $0.05/1M input and $0.15/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Nemotron Mini 4B Instruct or GPT-6.1 Sol?+
Nemotron Mini 4B Instruct 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.
Sources: NVIDIA & OpenAI Official Documentation