[whatmodel.sh](../../README.md) / Spark-X2.5 1.7B
# Spark-X2.5 1.7B
iFLYTEK (SparkLLM) · released 2026-08 · 1.71B params · apache-2.0
**score 54.7** ±0.7 · #291 of 732 · from 34 benchmark results across 2 sources · † 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-X2.5 1.7B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #214 of 313 31% 10
coding ▁▁▁▃▅●█▇▅▄▃▁ #320 of 469 19% 4
chat & writing ▁▁▁▃▆▄▆█●▇▇▃ #141 of 402* 2.1% 1
math ▁▁▃▅█●▄█▄▂▁▁ #198 of 431 33% 3
web search ▅·▅▅█·▃▃●·▅▃ #4 of 15 3.9% 2
agent tasks · #214 of 313 · 31% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 (Jan '25)](deepseek-r1.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Mellum2 12B-A2.5B Instruct](mellum2-12b-a2-5b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Pro (split unspecified)… 10.4% ▂·▁▁▁●▂▃█▇▂▁ beats 8/58 very hard · avg 9.8% 6.6% sharp · several runs
SWE-bench Verified (maker) 28.3% ▁▁▁▁▁▁●▄█▆▁▁ beats 16/138 very hard · avg 14% 5.2% sharp · small run · several runs
τ²-bench aggregate (maker) 65.3% ▁▁▁·▁▁▃●▃▄█▁ beats 12/39 medium · avg 54% 4.2% at its level · several runs
SWE-bench Multilingual (maker) 23.3% ▁··●▃▁▅██▂▂▁ beats 2/65 hard · avg 18% 4.1% several runs
BFCL V4 (December 2025 archive) (… 46.9% ▁▁▁▂▆▆▅●█▂▄▃ beats 65/114 medium · avg 40% 3.1% at its level · several runs
BrowseComp (maker) 29.7% ▅···█·▃···●▃ beats 6/7 hard · avg 21% 2.7% at its level · several runs
MCP-Atlas (public set) (maker) 23.4% ▂·●▂▂·▁▃█▆▆▂ beats 2/39 hard · avg 21% 2.2% at its level
MCPMark (maker) 2.3% ●·····█··▃█▃ beats 0/8 very hard · avg 5.5% 1.3% too hard for it
Humanity's Last Exam with tools (… 6.9% ●·▁▂▂▁▄▄▇█▄▅ beats 0/62 very hard · avg 11% 1.2% typical
τ³-bench (maker) 20.1% ▃·▃·█·▃··●·▃ beats 6/7 very hard · avg 13% 0.8% typical
coding · #320 of 469 · 19% of its score
Beats [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3 4B Instruct 2507](qwen3-4b-instruct-2507.md), [Qwen3-VL 8B Instruct](qwen3-vl-8b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Code Tulu 2 34B](code-tulu-2-34b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Pro (split unspecified)… 10.4% ▂·▁▁▁●▂▃█▇▂▁ beats 8/58 very hard · avg 9.8% 6.6% sharp · several runs
SWE-bench Verified (maker) 28.3% ▁▁▁▁▁▁●▄█▆▁▁ beats 16/138 very hard · avg 14% 5.2% sharp · small run · several runs
SWE-bench Multilingual (maker) 23.3% ▁··●▃▁▅██▂▂▁ beats 2/65 hard · avg 18% 4.1% several runs
SciCode (level unspecified) (make… 18.2% █·····●█████ beats 1/6 very hard · avg 14% 3.2% several runs
chat & writing · #141 of 402 · 2.1% of its score
Beats [o3-mini](o3-mini.md), [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md) here, though they rank higher overall. Trails [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) … 89.5% ▁▂▄▂▂▄▆█●▅▂▁ beats 111/145 easy · avg 83% 2.1% several runs
math · #198 of 431 · 33% 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
HMMT February 2026 (maker) 48.4% █·▄··●··▄█·▄ beats 3/7 hard · avg 32% 14% sharp · at its level · several runs
AIME 2026 (maker) 69.4% ▁▁▁▂●▂▆█▆▂·▁ beats 9/52 medium · avg 53% 11% sharp · at its level · several runs
IMO-AnswerBench (maker) 45.4% ▄▄···●·▄··█▄ beats 2/6 hard · avg 32% 8.5% at its level · several runs
web search · #4 of 15 · 3.9% 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) 29.7% ▅···█·▃···●▃ beats 6/7 hard · avg 21% 2.7% at its level · several runs
Humanity's Last Exam with tools (… 6.9% ●·▁▂▂▁▄▄▇█▄▅ beats 0/62 very hard · avg 11% 1.2% typical
other tests (5)
test result vs the others in its run difficulty counts why
GPQA (subset unspecified) (maker) 43.8% ●···▃···▅·▃▃ beats 1/7 hard · avg 37% 10% sharp · at its level · several runs
GPQA Diamond (maker) 46.8% ▁▁▁▁▂▄●▅███▄ beats 70/322 hard · avg 38% 7.7% sharp · at its level
Humanity's Last Exam (maker) 6.3% ▄▄·▄··●·▄·▄█ beats 3/7 very hard · avg 3.8% 6.1% too hard for it · several runs
IFBench (aggregation unspecified)… 66.3% ▂▆▂▄▄▄▂▄●▆█▇ beats 49/86 medium · avg 49% 3.4% at its level · several runs
LongBench v2 (maker) 33.2% ▂·▁·▂●▁▄▄▃█▄ beats 6/33 very hard · avg 32% 2.7% typical
source results share of the score
Spark-X2.6 model cards (submitted / mak… 17 54% ▇▇▇▇▇▇
XHToken/Spark-X2.5-4B model card (submi… 17 46% ▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 54.7
Licences: Spark-X2.6 model cards ([Apache-2.0](../../licenses/spark-x26.txt)) · XHToken/Spark-X2.5-4B model card ([Apache-2.0](https://huggingface.co/XHToken/Spark-X2.5-4B/blob/0bcb35678590218655dff3765b9e61c83b35e9c4/README.md))
## 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-X2.5 1.7B (Q4_K_M, needs ~1.11 GB, from [huggingface.co/XHToken](https://huggingface.co/XHToken/Spark-X2.5-1.7B-GGUF/tree/1f7fa33b1245c14730da39e125714ad3a327901b))
```sh
llama cli -hf XHToken/Spark-X2.5-1.7B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf XHToken/Spark-X2.5-1.7B-GGUF:Q4_K_M
```
### 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-X2.5 1.7B (Q4_K_M, needs ~1.11 GB, from [huggingface.co/XHToken](https://huggingface.co/XHToken/Spark-X2.5-1.7B-GGUF/tree/1f7fa33b1245c14730da39e125714ad3a327901b))
```sh
lms get https://huggingface.co/XHToken/Spark-X2.5-1.7B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
### 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-X2.5 1.7B (BF16, needs ~3.4 GB, from [ollama.com](https://ollama.com/SparkLLM/Spark-X2.5-1.7B/tags))
```sh
ollama run SparkLLM/Spark-X2.5-1.7B
```
Model repository and documentation: [huggingface.co/XHToken](https://huggingface.co/XHToken/Spark-X2.5-1.7B)
## Published speed measurements
Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine.
hardware precision speed engine and settings
AMD Radeon 890M (unified me… Q8_0 28.89 tok/s llama.cpp 4c9233c034fc, Vulkan · …
NVIDIA GeForce RTX 3090 Q8_0 314.12 tok/s llama.cpp 4c9233c034fc, CUDA · ll…
NVIDIA GeForce RTX 2080 Q8_0 166.01 tok/s llama.cpp 4c9233c034fc, CUDA · ll…
Each speed links to its source, which has the full settings.
[interactive version](https://whatmodel.sh/model/spark-x2-5-1-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)
