LLM Comparison
Command R (08-2024) vs Phi 4
Side-by-side specs, pricing & capabilities
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2/6 models| Organization | ||
| OpenTools Score | ||
| Family | Command | Phi |
| Status | Current | Current |
| Release Date | Aug 2024 | Jan 2025 |
| Context Window | 128K tokens | 16K tokens |
| Input Price | $0.15/M tokens | $0.07/M tokens |
| Output Price | $0.60/M tokens | $0.14/M tokens |
| Capabilities | textcode | textcode |
| Max Output | 4K tokens | 16K tokens |
| API Identifier | cohere/command-r-08-2024 | microsoft/phi-4 |
| Benchmarks | ||
| AIME 2025 | — | 18%artificial-analysis |
| GPQA Diamond | — | 57.5%artificial-analysis |
| View Command R (08-2024) | View Phi 4 | |
Cost Calculator
Enter your expected monthly token usage to compare costs.
| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Phi 4Cheapest | $0.07 | $0.07 | $0.14 | — |
| Command R (08-2024) | $0.15 | $0.30 | $0.45 | +233% |
Compare hosting configurations
Compare two exact setups, including different configurations of the same model. Hardware fit, measured speed and costs are separate decisions.
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Use hours for the same scenario period. Cloud billing includes idle time; active hours alone do not set the bill. Local electricity uses whole-system attributable power, powered hours and your tariff. Missing inputs remain unknown.
Measured speed needs the same pinned workload protocol, tokenizer, context, batching and cache state.
| Configuration | Run Command R (08-2024) Q4_K_M on a local CPU | Run Phi-4 locally on a Linux L40S server |
|---|---|---|
| Model | Command R (08-2024) | Phi 4 |
| Hardware | Intel Core i5-12400 desktop, 8 inference threads, 64 GiB RAM | One NVIDIA L40S in a Linux x86_64 server |
| Runtime | llama.cpp v0.7.0 (620d2225; full pin in source URL) · CPU | vLLM 0.31.0+cu129 · CUDA |
| Artifact | Command R (08-2024) Q4_K_M by Bartowski · Complete Q4_K_M mixed GGUF weights; no runtime quantization or native FP32 claim · Complete Bartowski Q4_K_M mixed GGUF quantization; not native FP32 | Phi-4 · BF16 · None; original BF16 weights |
| Context / concurrency | 4,096 tokens / 1 | 2,048 tokens / 1 |
| Required memory | Unknown (unknown) | Unknown (unknown) |
| Evidence | Documented setup | Documented setup |
| Hardware fit | Hardware fit unknown No process peak measured for complete Q4_K_M weights, F16 attention cache, scratch, runtime and OS. Enter available memory per device or unified memory pool. Host RAM: 64 GiB selected host capacity is a planning allocation, not a measured minimum. Free disk space: 80 GiB selected disk capacity is a planning allocation, not measured disk peak. | Hardware fit unknown Complete GPU footprint has not been measured: model weights, KV cache, runtime allocations, compilation and workspace must all fit. Host RAM: No measured minimum host RAM for this exact version, context and configuration is available. Free disk space: Known download size is not the complete environment, cache and runtime disk requirement. |
| One-time cost | Unknown hardware: Existing desktop purchase price and incremental hardware cost depend on the reader; unknown. setup: Initial provisioning/build time and labor cost are unknown. | Unknown Hardware purchase: No dated hardware purchase quote is available; existing ownership is not treated as a zero purchase price. Initial setup work: Initial setup time is not priced; no labour rate or duration has been measured. |
| Recurring scenario cost | Unknown electricity: Actual wall power, electricity rate and powered hours are unknown; no TDP-to-wall-power conversion. storage: Incremental cost of local disk capacity is unknown. The 80 GiB free-disk walkthrough allocation is an author choice, not a measured requirement. egress: Data transfer price, allowances and traffic volume are unknown. administration: Maintenance labor rate and hours are unknown. One-time setup and recurring costs are separate. Unknown components are not treated as free. No monthly token output is inferred from peak throughput. | Unknown Electricity: Whole-system measured wall power, powered-on hours and the reader’s electricity tariff are unknown. GPU maximum board power is not a measured system draw. Operations and maintenance: Ongoing patching and operation effort is not measured or priced. Persistent storage: The owned host storage arrangement and any recurring backup or storage bill are unknown; no monthly amount is asserted. Data transfer: The reader’s internet or network plan and traffic volume are unknown; loopback requests alone do not establish total network costs. One-time setup and recurring costs are separate. Unknown components are not treated as free. No monthly token output is inferred from peak throughput. |
| Published measurements (unranked) | No compatible measurement available | No compatible measurement available |
Different currencies and incomplete costs are not ranked. Model quality benchmarks remain on the model pages; they do not establish quality or speed for every deployment.
Cohere
Command R (08-2024)
Command R (08-2024) is a large language model from Cohere. Supports up to 128,000 token context window. Achieves 68.4% on MMLU. Available from $0.15/M input tokens.
Microsoft
Phi 4
Phi 4 is a large language model from Microsoft. Supports up to 16,384 token context window. Available from $0.07/M input tokens.
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