Kimi K2.6
A trillion-parameter agentic MoE with 32B active. Needs 512 GB or more even at 4-bit. Included because it is one of the strongest open-weight models for tool use, and because the fit calculator's answer for most hardware here is a clear no.
Moonshot AIMixture of expertsagenticreasoning2 machines can run it
Total parameters
1.0T
Official spec sets memory need
Active parameters
32B
Official spec sets decode speed
Context
256K
Official spec tokens
4-bit weights
559 GB
Calculated before KV cache
This is a sparse mixture of experts. All 1.0T of weights must be available to the runtime, but only 32B are read per generated token. Ordinary runtimes keep the full quantized model in memory; a runtime-specific SSD-streaming artifact can retain a smaller working set and fetch expert data on demand, trading speed for capacity.
Model specification
Publisher
Moonshot AI Official spec
Architecture
Sparse mixture of experts Official spec
Total parameters
1.0T Official spec
Active parameters
32B per token Official spec
Context length
262,144 tokens Official spec
Attention ⓘ
MLA · 64 KV heads × 192 dim × 61 layers Official spec
Licence
Modified MIT Official spec source
Specification confidence ⓘ
High · verified 2026-10-19 Official spec source
Released
1 Jun 2026 Official spec
Official source
Quantizations and memory
| Quantization | Format | Bits/weight | Resident | Download | Quality kept |
|---|---|---|---|---|---|
MLX 4-bit | mlx | 4.5 | 559.5 GB | 559.5 GB | 98.0% |
Q4_K_Mdefault | gguf | 4.85 | 597.2 GB | 597.2 GB | 98.5% |
Q5_K_M | gguf | 5.69 | 700.6 GB | 700.6 GB | 99.3% |
Q8_0 | gguf | 8.5 | 1036.5 GB | 1036.5 GB | 99.9% |
Generic weight sizes are computed from the parameter count and bits per weight plus a format-specific overhead. Runtime-specific artifacts use their published resident and download footprints; streamed models can therefore require much more disk than memory. Quality retention is an assumption, not a measured evaluation.
Coding & quality benchmarksCompare coding results →
| Benchmark | Category | Score | Run details | Reported by | Date | Source |
|---|---|---|---|---|---|---|
| Terminal-Bench 2.0 | agentic | 66.7 | Not supplied by source | canonical model_repository | 20 Apr 2026 | link |
| SWE-bench Pro | coding | 58.6 | Not supplied by source | canonical model_repository | 20 Apr 2026 | link |
| SWE-bench Verified | coding | 80.2 | Not supplied by source | canonical model_repository | 20 Apr 2026 | link |
| GPQA | reasoning | 90.5 | Not supplied by source | canonical model_repository | 20 Apr 2026 | link |
We show source metrics rather than deriving one opaque quality number. Different benchmarks measure genuinely different things, and collapsing them into a single score would hide exactly the disagreements worth seeing.
Recommendations
Cheapest that can run it
~36.6–52.6 t/s · $90,082
Lowest purchase price among configurations where the model fits at some quantization in our catalogue. Speed is not considered.
Cheapest above 20 t/s
~36.6–52.6 t/s · $90,082
20 tokens/second is roughly the point at which generation keeps pace with reading. Below it, interactive use feels like waiting.
Cheapest above 40 t/s
~36.6–52.6 t/s · $90,082
40 tokens/second is the threshold most people describe as comfortable for coding agents, where output arrives faster than you can review it.
Fastest with real measurements
Nothing in the database qualifies.
Highest throughput among configurations with an actual published measurement rather than our estimate.
Best throughput per purchase-price unit
~345–496 t/s · $359,430
Highest decode tokens/second per 1,000 units of the displayed purchase currency. Ignores running costs and resale — see the economics section for the full picture.
Hardware that runs Kimi K2.60 measured, 2 estimated
2 of 2 rows
| Hardware↕ | Quant | Memory↕ | Decode▼ | Prefill↕ | Context | Price↕ | Fit | Confidence↕ |
|---|---|---|---|---|---|---|---|---|
| NVIDIA DGX H200 (8x H200, 1,128 GB) NVIDIA · 1,128 GB · 38,400 GB/s | Q5_K_Mrecommended | 726.0 GB | ~345–496 t/s | ~14760–30660 t/s | 256K | $359,430 | Comfortable | Estimated |
| NVIDIA DGX Station GB300 (748 GB) NVIDIA · 748 GB · 7,100 GB/s | Q4_K_Mrecommended | 616.6 GB | ~36.6–52.6 t/s | ~1970–4100 t/s | 8K | $90,082 | Borderline | Estimated |
Submit a benchmarkContributions are reviewed before publication.
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