Kimi K3.1 Pro vs DeepSeek V4.1 Flash vs GLM-5.3: Which Open Model for Fintech Agents
Kimi K3.1 Pro, DeepSeek V4.1 Flash and GLM-5.3 compared for AI agents in payments and fintech: specs, licenses, reported benchmarks, cost at volume, and which to pick for each job.
Short answer: for most fintech agents, start with DeepSeek V4.1 Flash. It is cheap to run, MIT licensed and reads images. Move the hardest, document-heavy cases to Kimi K3.1 Pro, the most capable open model in our list. Use GLM-5.3 when a run chains many tools and the inputs are plain text.
Three open-weight models now sit at the top of most rankings, and they were released within ten weeks of each other. Each one wins somewhere. The useful question for a payments team is not which model is best, but which model each job should run on.
This is a guide to choosing between them for agents in payment operations, risk and compliance. It uses the specs from our open source model comparison and the benchmark table Moonshot published with Kimi K3.1 Pro.
What it does. Puts the three models side by side, maps them to the jobs fintech agents do, and gives you a short method for testing them on your own cases.
What it is not. An independent benchmark. Every score here is reported by a model provider. The model you ship is the one that passes your evals.
The three models at a glance
| Kimi K3.1 Pro | DeepSeek V4.1 Flash | GLM-5.3 | |
|---|---|---|---|
| Provider | Moonshot AI | DeepSeek | Z.ai |
| Open weights since | November 2, 2026 | October 17, 2026 (1017 checkpoint) | August 25, 2026 |
| Size | 2.8T total, 104B active | 552B total, 8B active | 753B total |
| Context | 1M tokens | 1M tokens | 1M tokens |
| Input | Text, images | Text, images | Text |
| License | Kimi K3.1 Pro License (custom) | MIT | GLM-5.3 License (custom) |
| In one line | Most capable | Best overall value | Built for long tool-heavy runs |
Active parameters drive cost. Each token in DeepSeek V4.1 Flash runs through 8B parameters; each token in Kimi K3.1 Pro runs through 104B. On your own hardware or a per-token API, that gap shows up directly in cost per run.
What the reported benchmarks say
Moonshot's model card collects scores for all three models in one table. Its footnotes combine Moonshot evaluations, other providers' reports, and external leaderboards, so this is a reported-score comparison, not a controlled like-for-like evaluation. Kimi K3.1 Pro's Terminal-Bench 4.0 and DeepSWE v1.1 results use the Claude Code harness.
| Benchmark (higher is better) | Kimi K3.1 Pro | DeepSeek V4.1 Flash | GLM-5.3 |
|---|---|---|---|
| Terminal-Bench 4.0 (agentic coding in a terminal) | 65.7 | 42.3 | 41.8 |
| DeepSWE v1.1 (software engineering) | 75.5 | 77.0 | 66.9 |
| Automation Bench-AA (business automation) | 73.3 | 66.6 | 62.2 |
| GDPval-AA v2.1 (knowledge work, Elo) | 1846 | 1746 | 1650 |
| OfficeQA Pro (questions over office documents) | 67.4 | 62.4 | 58.6 |
Source: the Kimi K3.1 Pro model card. Scores are provider-reported and not verified by Runtime.
Two things stand out. Kimi K3.1 Pro leads on long, multi-step work, and the gap is widest on Terminal-Bench, where an agent has to plan and recover across many commands. DeepSeek V4.1 Flash matches or beats it on plain coding while running a fraction of the parameters.
Which model for which job
| Job | Start with | Why |
|---|---|---|
| Transaction monitoring and alert triage | DeepSeek V4.1 Flash | High volume, short runs, cost per alert matters most |
| Payment reconciliation | DeepSeek V4.1 Flash | Strong on code and data work, MIT license |
| Chargebacks and dispute packets | Kimi K3.1 Pro | Reads receipts and screenshots, best at long document work |
| Partner-bank RFIs | Kimi K3.1 Pro | Long PDFs and scans, accuracy over cost |
| Merchant underwriting | GLM-5.3 or Kimi K3.1 Pro | Long investigations across many tools and sources |
| KYC and KYB | DeepSeek V4.1 Flash | Image input, permissive license, cheap at onboarding volume |
Most teams end up with two models: a cheap default for the queue and a stronger model for the cases that escalate. Routing by job beats picking one winner.
How to choose with your own cases
- Pull 30 to 50 resolved cases for one job, with the answer your team gave. Include the messy ones.
- Run each model on the same cases with the same skill and tools. Keep the harness fixed so you compare models, not prompts.
- Score against your answers, not against a benchmark. Track accuracy, steps taken and cost per run.
- Set the default and the escalation. The cheapest model that passes becomes the default; the strongest handles what it flags as low confidence.
- Rerun when a new model ships. This list changed three times in ten weeks.
A prompt that sets this up:
Run the 40 resolved chargeback cases in the evals folder on DeepSeek V4.1 Flash and Kimi K3.1 Pro with the current dispute skill. For each case, compare the packet to the one our analyst submitted and score it. Report accuracy, average steps and cost per run for each model, and list the cases where they disagree.
Say why, not just what: telling the agent the goal is choosing a default model makes it report the numbers you need for that decision.
What stays human
| Step | Why |
|---|---|
| Choosing which cases go in the eval set | It encodes what "right" means for your team |
| Approving a model for production | Legal reviews the license, risk reviews the data path |
| Decisions that move money or answer a customer | An agent proposes, a person approves |
The point
Open-weight models are now good enough that the choice is about fit, not quality. DeepSeek V4.1 Flash is the default for most queues, Kimi K3.1 Pro is the model for hard cases, and GLM-5.3 earns its place on long text-only runs. The ranking will change again; the eval set you build is what lets you switch in an afternoon.
Back to testing.
