[whatmodel.sh](../../README.md) / Spark-X2.6 1.7B
# Spark-X2.6 1.7B
iFLYTEK (SparkLLM) · released 2026-10 · 1.708B params · apache-2.0
**score 58.6** ±1.0 · #217 of 732 · from 17 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Spark-X2.6 1.7B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄●▅▄▄▂▁ #140 of 313 28% 7
coding ▁▁▁▃▅▇█●▅▄▃▁ #172 of 469 19% 4
chat & writing ▁▁▁▃▆▄▆█●▇▇▃ #149 of 402* 2.8% 1
math ▁▁▃▅█●▄█▄▂▁▁ #199 of 431 25% 3
web search ▅·▅▅█·▃▃▃·●▃ #3 of 15* 6.2% 2
agent tasks · #140 of 313 · 28% of its score
Beats [o3](o3.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Pro (split unspecified)… 26.7% ▂·▁▁▁▁●▃█▇▂▁ beats 13/58 very hard · avg 9.8% 8.2% sharp
SWE-bench Verified (maker) 59.8% ▁▁▁▁▁▁▂▄●▆▁▁ beats 56/138 hard · avg 19% 3.9% sharp · at its level
BrowseComp (maker) 41.7% ▂·▃▁▂▂●▅█▂▂▄ beats 15/46 hard · avg 21% 3.7% at its level
τ²-bench aggregate (maker) 85.3% ▁▁▁·▁▁▃▃▃▄●▁ beats 29/39 medium · avg 54% 3.7% at its level
SWE-bench Multilingual (maker) 57.3% ▁··▁▃▁●██▂▂▁ beats 19/65 hard · avg 25% 3.4% at its level
BFCL V4 (December 2025 archive) (… 55.2% ▁▁▁▂▆▆▅▆●▂▄▃ beats 85/114 medium · avg 40% 3.1% at its level
Humanity's Last Exam with tools (… 19.9% ▁·▁▂●▁▄▄▇█▄▅ beats 7/62 very hard · avg 11% 2.5% typical
coding · #172 of 469 · 19% of its score
Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Grok 4.1 Fast (Reasoning)](grok-4-1-fast-reasoning.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [Phi-4-reasoning 14B](phi-4-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Pro (split unspecified)… 26.7% ▂·▁▁▁▁●▃█▇▂▁ beats 13/58 very hard · avg 9.8% 8.2% sharp
SWE-bench Verified (maker) 59.8% ▁▁▁▁▁▁▂▄●▆▁▁ beats 56/138 hard · avg 19% 3.9% sharp · at its level
SWE-bench Multilingual (maker) 57.3% ▁··▁▃▁●██▂▂▁ beats 19/65 hard · avg 25% 3.4% at its level
SciCode (level unspecified) (make… 20.1% ▁····●▃▄▅█▆▂ beats 3/40 very hard · avg 14% 3.4% typical
chat & writing · #149 of 402 · 2.8% of its score
Beats [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md), [Nemotron 3 Super 120B-A12B](nemotron-3-super-120b-a12b.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) … 89.4% ▁▂▄▂▂▄▆█●▅▂▁ beats 110/145 easy · avg 83% 2.8% typical
math · #199 of 431 · 25% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [Qwen3 235B-A22B (Apr 2025, hybrid)](qwen3-235b-a22b.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2026 (maker) 70.1% ▁▁▁▂●▂▆█▆▂·▁ beats 10/52 medium · avg 53% 9.1% sharp
HMMT February 2026 (maker) 48.8% ▃▃▂●·████▅▃▅ beats 6/35 hard · avg 32% 8.6% sharp · at its level
IMO-AnswerBench (maker) 47.1% ▁▁·●▁▁▄▆█▄▂▃ beats 4/44 hard · avg 32% 7.4% at its level
web search · #3 of 15 · 6.2% of its score
Beats [MiniCPM5 2B](minicpm5-2b.md), [Qwen3.5 9B](qwen3-5-9b.md), [Gemma 4 12B](gemma-4-12b.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
BrowseComp (maker) 41.7% ▂·▃▁▂▂●▅█▂▂▄ beats 15/46 hard · avg 21% 3.7% at its level
Humanity's Last Exam with tools (… 19.9% ▁·▁▂●▁▄▄▇█▄▅ beats 7/62 very hard · avg 11% 2.5% typical
other tests (5)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 56.1% ▁▁▁▁▂▄▅●███▄ beats 103/322 hard · avg 38% 15% sharp · at its level
GPQA (subset unspecified) (maker) 51.3% ▂▁▂▂▄▃▆●▅█▃▃ beats 53/106 hard · avg 37% 10% sharp · at its level
LongBench v2 (maker) 40.4% ▂·▁·▂▃▁●▄▃█▄ beats 11/33 very hard · avg 32% 5.8% typical
Humanity's Last Exam (maker) 8.7% ▁▁▁▄●▆▇██▆▁▃ beats 16/111 very hard · avg 3.8% 5.1% typical
IFBench (aggregation unspecified)… 65.5% ▂▆▂▄▄▄▂▄●▆█▇ beats 48/86 medium · avg 49% 3.5% at its level
source results share of the score
Spark-X2.6 model cards (submitted / mak… 17 100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 58.6
Licences: Spark-X2.6 model cards ([Apache-2.0](../../licenses/spark-x26.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Spark-X2.6 1.7B (Q4_K_M, needs ~1.7687 GB, from [huggingface.co/XHToken](https://huggingface.co/XHToken/Spark-X2.6-1.7B-GGUF/tree/cdc3d8893fba4ce0687f1b49ce1e18b29cae96d0))
```sh
llama cli -hf XHToken/Spark-X2.6-1.7B-GGUF
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf XHToken/Spark-X2.6-1.7B-GGUF
```
Other versions: Q8_0 (~1.8201 GB) `llama cli -hf XHToken/Spark-X2.6-1.7B-GGUF` · BF16 (~3.4209 GB) `llama cli -hf XHToken/Spark-X2.6-1.7B-GGUF`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Spark-X2.6 1.7B (Q4_K_M, needs ~1.7687 GB, from [huggingface.co/XHToken](https://huggingface.co/XHToken/Spark-X2.6-1.7B-GGUF/tree/cdc3d8893fba4ce0687f1b49ce1e18b29cae96d0))
```sh
lms get https://huggingface.co/XHToken/Spark-X2.6-1.7B-GGUF -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~1.8201 GB) `lms get https://huggingface.co/XHToken/Spark-X2.6-1.7B-GGUF -y` · BF16 (~3.4209 GB) `lms get https://huggingface.co/XHToken/Spark-X2.6-1.7B-GGUF -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Spark-X2.6 1.7B (Q4_K_M, needs ~1.7687 GB, from [huggingface.co/XHToken](https://huggingface.co/XHToken/Spark-X2.6-1.7B-GGUF/tree/cdc3d8893fba4ce0687f1b49ce1e18b29cae96d0))
```sh
ollama run hf.co/XHToken/Spark-X2.6-1.7B-GGUF
```
Other versions: Q8_0 (~1.8201 GB) `ollama run hf.co/XHToken/Spark-X2.6-1.7B-GGUF` · BF16 (~3.4209 GB) `ollama run hf.co/XHToken/Spark-X2.6-1.7B-GGUF`
Model repository and documentation: [huggingface.co/XHToken](https://huggingface.co/XHToken/Spark-X2.6-1.7B)
[interactive version](https://whatmodel.sh/model/spark-x2-6-1-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)
