OpenAI (67 models) vs Microsoft (3 models) - compared across composite scores, pricing, capabilities, and context windows.
| OpenAI | Score | vs | Microsoft | Score |
|---|---|---|---|---|
| GPT-5.4 Pro | 92 | Phi 4 | 60 | |
| GPT-5.4 | 92 | Phi 4 Mini Instruct | 53 | |
| GPT-5.2 Pro | 91 | WizardLM-2 8x22B | 28 |
| Capability | OpenAI | Microsoft | Leader |
|---|---|---|---|
Vision | 45/67 | 0/3 | OpenAI |
Reasoning | 37/67 | 0/3 | OpenAI |
Function Calling | 57/67 | 0/3 | OpenAI |
JSON Mode | 63/67 | 2/3 | OpenAI |
Web Search | 28/67 | 0/3 | OpenAI |
Streaming | 65/67 | 3/3 | OpenAI |
Image Output | 4/67 | 0/3 | OpenAI |
| Metric | OpenAI | Microsoft |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.030 gpt-oss-20b | $0.065 Phi 4 |
| Cheapest Output (per 1M tokens) | $0.140 | $0.140 |
| Most Expensive Input (per 1M tokens) | $150.00 o1-pro | $0.620 WizardLM-2 8x22B |
| Most Expensive Output (per 1M tokens) | $600.00 | $0.620 |
| Free Models | 2 | 0 |
| Max Context Window | 1.1M | 128K |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| GPT-5.4 Pro | 92 | $30.00 | $180.00 |
| GPT-5.4 | 92 | $2.50 | $15.00 |
| GPT-5.2 Pro | 91 | $21.00 | $168.00 |
| GPT-5.2-Codex | 90 | $1.75 | $14.00 |
| GPT-5.2 | 90 | $1.75 | $14.00 |
| GPT-5.3-Codex | 89 | $1.75 | $14.00 |
| GPT-5 Pro | 89 | $15.00 | $120.00 |
| GPT-5.1-Codex-Max | 88 | $1.25 | $10.00 |
| GPT-5 Codex | 88 | $1.25 | $10.00 |
| GPT-5 | 88 | $1.25 | $10.00 |
| GPT-5.3 Chat | 87 | $1.75 | $14.00 |
| GPT-5.1 | 87 | $1.25 | $10.00 |
| GPT-5.1-Codex | 87 | $1.25 | $10.00 |
| GPT-5.1-Codex-Mini | 87 | $0.250 | $2.00 |
| o3 Deep Research | 87 | $10.00 | $40.00 |
| o3 Pro | 87 | $20.00 | $80.00 |
| o3 | 87 | $2.00 | $8.00 |
| GPT-5.1 Chat | 87 | $1.25 | $10.00 |
| o4 Mini Deep Research | 81 | $2.00 | $8.00 |
| o4 Mini | 81 | $1.10 | $4.40 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Phi 4 | 60 | $0.065 | $0.140 |
| Phi 4 Mini Instruct | 53 | $0.080 | $0.350 |
| WizardLM-2 8x22B | 28 | $0.620 | $0.620 |
Compare any two AI providers side-by-side.
Microsoft's narrow portfolio of just 2 models (both open source) with a top score of 32/100 suggests they're focusing on lightweight, specialized solutions rather than competing across the full capability spectrum. Their Phi models target edge computing and cost-sensitive deployments at $0.140-$0.620 per 1M tokens, while OpenAI's 64-model lineup spans from ultra-cheap ($0.110/M) to premium ($600/M), covering everything from basic chat to advanced reasoning with scores up to 67/100.
Microsoft's Phi models are optimized for basic text generation and code completion in resource-constrained environments, with a 66K context window that's 17x smaller than OpenAI's 1.1M maximum. At 29/100 average score versus OpenAI's 49/100, Microsoft appears to be betting on scenarios where deployment efficiency matters more than raw capability - think embedded systems, mobile apps, or high-volume simple tasks where the $0.140/M pricing beats OpenAI's flagship models by 100-4000x.
OpenAI's extensive vision support enables multimodal applications across 42 models, from GPT-5.4 (67/100 score) down to budget options, while Microsoft users must integrate separate vision APIs or switch providers entirely. This gap is particularly striking given that 53% of OpenAI's models support reasoning (34/64) and 89% support function calling (57/64), enabling complex workflows that combine visual understanding with tool use - capabilities completely absent from Microsoft's current offerings.
OpenAI's free tier provides zero-cost prototyping and testing across 2 models, while Microsoft requires immediate budget commitment starting at $0.140 per 1M tokens. Combined with OpenAI's 5 open source models versus Microsoft's 2, developers get more flexibility to experiment locally or deploy without API dependencies, though Microsoft's entire portfolio being open source (100% vs OpenAI's 7.8%) offers different advantages for self-hosting scenarios.
Microsoft's Phi 4 at $0.620/M tokens actually costs more than 31 of OpenAI's models while scoring 35 points below GPT-5.4, but its value proposition lies in being fully open source with presumably lower latency and better integration with Azure services. For organizations already invested in Microsoft's ecosystem, running Phi models on Azure infrastructure might offer better TCO than OpenAI's $0.110/M budget options, despite the 17-point score disadvantage and missing capabilities like vision and function calling.