North Mini Code vs GPT-5.1 Codex mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
North Mini Code by Cohere and GPT-5.1 Codex mini by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, North Mini Code holds a notable pricing advantage for token ingestion. On output generation, North Mini Code is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.1 Codex mini provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5.1 Codex mini leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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North Mini Code
Modello a Pesi ApertiNorth Mini Code di Cohere è un modello di intelligenza artificiale con finestra di contesto da 256K token e tariffazione API di $0.2/1M token in input e $0.6/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.20 / 1M
$0.60 / 1M
256K tokens
45%
GPT-5.1 Codex mini
Modello di Ragionamento ProfondoGPT-5.1 Codex mini di OpenAI è un modello di intelligenza artificiale con finestra di contesto da 400K token e tariffazione API di $0.25/1M token in input e $2/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.25 / 1M
$2.00 / 1M
400K tokens
55.5%
Differenze Chiave a Colpo d’Occhio
- North Mini Code is 2.5x cheaper for API usage (input + output tokens combined).
- GPT-5.1 Codex mini has a larger context window (400K vs 256K tokens).
- GPT-5.1 Codex mini is faster at 180 tok/s vs 125 tok/s.
- GPT-5.1 Codex mini leads on SWE-bench coding (55.5% vs 45.0%).
| Feature | North Mini Code | GPT-5.1 Codex mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 47 | 47 |
Provider | Cohere | OpenAI |
Input Price Cost per million input tokens | $0.20 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.13 / 1M | $0.27 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 125 tok/s | 180 tok/s |
SWE-bench Verified Real-world software engineering | 45% | 55.5% |
GPQA Diamond Graduate-level reasoning | 55% | 66.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $0.03 / 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.
North Mini Code · Strengths & Key Advantages
- Architettura Code-Specialized Autoregressive Transformer avanzata di Cohere con supporto fino a 256K token di contesto
- Ottimo rapporto qualità-prezzo ($0.2/1M input, $0.6/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
GPT-5.1 Codex mini · Strengths & Key Advantages
- Architettura Compact code-specialised reasoning model avanzata di OpenAI con supporto fino a 400K token di contesto
- Ottimo rapporto qualità-prezzo ($0.25/1M input, $2/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | North Mini Code | GPT-5.1 Codex mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.15 | $0.33 |
Output Tokens / Task Tokens generated per task | 30K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 12K | 23K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | North Mini Code | GPT-5.1 Codex mini |
|---|---|---|
| Official API Model ID (SDK) | north-mini-code | gpt-5-1-codex-mini |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-06 | 2025-11 |
| Knowledge Cutoff | April 2026 | October 2024 |
| Architecture | Code-Specialized Autoregressive Transformer | Compact code-specialised reasoning model |
| Parameters | Confidential (~20B) | 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.2/1M in) across full 256K window | Flat rate ($0.25/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Cached input at $0.04/1M tokens | Automatic prefix caching at $0.025/1M (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) · 12 GB VRAM (4-bit Quantized) · 43 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 |
North Mini Code vs GPT-5.1 Codex mini: Risposte Rapide (FAQ)
Which model is faster?
GPT-5.1 Codex mini is faster, generating 180 tokens per second compared with 125 tokens per second.
Which model is cheaper to run?
North Mini Code is cheaper at $0.13 per 1M tokens blended, compared with $0.27 (7:2:1 cache:input:output ratio).
Which model has lower latency?
North Mini Code has lower latency, with a time to first token of 0.32s compared with 0.72s.
Which model has a larger context window?
GPT-5.1 Codex mini has a larger context window, supporting 400K tokens compared with 256K tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose North Mini Code if your priority is Developer IDE copilots, automated unit test suites, and fast terminal CLI coding agents. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.1 Codex mini if your workflow prioritizes High-volume code automation and inline review. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does North Mini Code compare to GPT-5.1 Codex mini in API token pricing and cost per token?+
North Mini Code costs $0.20 per 1M input tokens and $0.60 per 1M output tokens, whereas GPT-5.1 Codex mini costs $0.25 input and $2.00 output. Overall, North Mini Code is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: North Mini Code or GPT-5.1 Codex mini?+
North Mini Code supports a context window of 256K tokens (~192k words), while GPT-5.1 Codex mini supports 400K tokens (~300k words). Therefore, GPT-5.1 Codex mini can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: North Mini Code or GPT-5.1 Codex mini?+
North Mini Code scores 45% on SWE-bench Verified while GPT-5.1 Codex mini scores 55.5%. For autonomous software engineering and multi-file debugging, GPT-5.1 Codex mini holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between North Mini Code and GPT-5.1 Codex mini?+
North Mini Code features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.1 Codex mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between North Mini Code and GPT-5.1 Codex mini?+
North Mini Code generates approximately 125 tokens per second, while GPT-5.1 Codex mini delivers 180 tokens per second. In latency-sensitive production environments, GPT-5.1 Codex mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining North Mini Code and GPT-5.1 Codex mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to North Mini Code to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.1 Codex mini. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs