All five models in this roundup carry a quality score of 100 in our data, so the score alone will not separate them. The real differences are in what each model was built to do and in what it costs to run at volume. This guide compares the five on those two points so you can match a model to a workload.
The Lineup at a Glance
| Model | Input $/M tokens | Output $/M tokens | Primary focus |
|---|---|---|---|
| MoonshotAI: Kimi K3.1 Pro | $2.00 | $10.00 | Long-horizon coding and reasoning, 1M context |
| MiniMax: MiniMax M3.1 Flash | $0.15 | $0.60 | Agentic tasks, coding, cybersecurity |
| DeepSeek: DeepSeek V4.1 Flash 1017 | $0.15 | $0.60 | Sparse mixture-of-experts generalist |
| Elorian: Glimpse | $0.60 | $3.00 | Visual reasoning over images and video |
| Meta: Muse Spark Open | $1.25 | $4.25 | Agentic coding, open weights |
The price spread is wide. Kimi K3.1 Pro's output rate is about 16 times that of the two Flash models. For a sample workload of 10 million input tokens and 2 million output tokens, the costs come out to roughly $40 for Kimi K3.1 Pro, $21 for Muse Spark Open, $12 for Glimpse, and $2.70 for either Flash model. Those numbers should shape your defaults.
Kimi K3.1 Pro: The Pick for Very Long Context
Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model. It is a post-trained successor to Kimi K3, and it supports a 1M-token context window. Moonshot positions it for long-horizon coding and 3D work.
The 1M-token window is the main reason to pay its premium. If your task requires the model to hold an entire large codebase, a long document set, or an extended multi-step session in context at once, Kimi K3.1 Pro is built for that job. The open weights also matter for teams that want the option of hosting the model themselves.
Pick it when context length and long, multi-step reasoning are the bottleneck. Do not pick it as a default for high-volume, short-prompt traffic, because the $10 per million output tokens adds up quickly.
MiniMax M3.1 Flash: Cheap Agentic Work
MiniMax describes M3.1 Flash as a multimodal model with significant improvements over M3 across multiple benchmarks, particularly in complex agentic tasks, coding, and cybersecurity. At $0.15 input and $0.60 output, it ties for the lowest price in this group.
That combination makes it a strong candidate for agent loops, where a single user request can trigger dozens of model calls. Cheap tokens let you run more steps, more retries, and more evaluation passes without the bill growing out of control. The cybersecurity emphasis also makes it worth testing for security tooling, such as triaging findings or reviewing code for vulnerabilities.
Use it as the workhorse for agent pipelines and coding assistance where volume is high. Escalate to a pricier model only for the tasks where it measurably falls short in your own tests.
DeepSeek V4.1 Flash 1017: A Pinned Snapshot at the Same Low Price
DeepSeek V4.1 Flash 1017 is the October 17 snapshot of DeepSeek V4.1 Flash, served under the `deepseek-flash` API model name. It is a sparse mixture-of-experts model, and it matches MiniMax M3.1 Flash exactly on price.
The snapshot naming is the practical detail to notice. A dated snapshot gives you a fixed version to build against, which helps when you need consistent behavior across a test suite or a production release. Check how the `deepseek-flash` alias behaves in your setup, since an alias and a dated snapshot may not always point to the same thing over time.
Because it costs the same as MiniMax M3.1 Flash, the right move is to run both against your own prompts and keep whichever performs better on your data. At this price, an A/B test is inexpensive.
Elorian Glimpse: The Specialist for Images and Video
Glimpse is a multimodal visual reasoning model from Elorian. It is built for visual understanding and complex visual reasoning in the physical world, and it reasons directly over images and video.
This is the most specialized model in the group, and that is its value. If your product depends on interpreting what is happening in photos or video footage, such as inspecting physical scenes or reasoning about objects and their relationships, Glimpse is the model designed around that task. At $0.60 input and $3.00 output, it sits in the middle of the price range.
Do not use Glimpse as a general text or coding model. Route visual inputs to it and send everything else to a cheaper generalist.
Meta Muse Spark Open: Open-Weight Coding Agent
Muse Spark Open is a coding-optimized open-weight model trained specifically for agentic coding workflows. Meta describes it as delivering higher first-attempt accuracy, reliable tool calling, and deep codebase understanding.
Its description centers on code rather than visual input, so treat it as a coding model first. Reliable tool calling is a practical advantage in agent setups, because failed or malformed tool calls waste tokens and break pipelines. At $1.25 input and $4.25 output, it costs less than Kimi K3.1 Pro while targeting overlapping coding work, and its open weights give you a self-hosting path.
Choose it for coding agents where first-attempt accuracy saves you retries. If you do not need Kimi's 1M-token window, Muse Spark Open is the cheaper of the two premium coding options.
How to Assign These Models in Practice
A sensible setup uses routing rather than a single model. Send high-volume agent and coding calls to MiniMax M3.1 Flash or DeepSeek V4.1 Flash 1017, after testing both on your own prompts. Route image and video inputs to Glimpse. Reserve Muse Spark Open for coding agents where tool-call reliability matters, and send only the tasks that truly need a 1M-token context to Kimi K3.1 Pro.
Before committing, log a week of real traffic, replay it through your top two candidates for each task type, and compare both output quality and total token cost. That replay will tell you more than any shared quality score can.