Most inference platforms stock 100+ models. We carry four.
Most inference platforms stock 100+ models. We carry four. Here's why, and how to pick the right one for your workload.
Browse any inference platform and you'll see the same thing: a wall of models. Fifty. A hundred. More. The implicit pitch is that more choice means better outcomes.
It doesn't. It means more evaluation work, more "which model do I use?" paralysis, and more chances to pick something mediocre. Most of those models are filler, legacy weights that shouldn't be running in production, proprietary APIs that lock you in, or mid-tier models that are neither the smartest nor the cheapest.
You didn't want a menu. You wanted the right answer.
In economics, the efficient frontier is the set of portfolios that deliver the maximum return for a given level of risk. Anything below the frontier is suboptimal, you could get more return for the same risk, or the same return for less.
The same concept applies to inference models. Plot intelligence on the Y-axis and cost on the X-axis. Draw the line where you get . That's the efficient frontier.
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Everything above it is overpaying. Everything below it is underperforming. The models on or above that line are the only ones worth running.
We only carry models on or above that line. No filler. No legacy. No lock-in.
Every model on Telnyx Inference is open-weight and best-in-class at something. Here's what each one is built for and when to reach for it.
Kimi K2.6 is the model to reach for when you're building voice agents or real-time applications. Its non-reasoning mode is still highly intelligent, you don't have to trade smarts for speed, and it delivers lower time to first token (TTFT) than GLM-5.1, which is the metric that matters most for voice. When a user speaks to an agent, every millisecond of first-token delay is dead air. Kimi minimizes that gap without dumbing down the output.
In our benchmarks, Kimi is the most competitive model across providers, the race is close. That's fine. We don't need to win every cell. We need to offer the right models for the right jobs, and Kimi is the right model when you need speed and intelligence in a real-time context.
Best for: Voice AI, real-time conversational agents, any workload where TTFT determines whether the experience works.
GLM-5.1 is not the right model for real-time voice, its TTFT is higher than Kimi K2.6, and in a voice context that gap is felt as dead air. But for workloads where throughput and structured output matter more than first-token speed, GLM-5.1 is the strongest option on the platform.
It excels at function calling, tool use, and batch reasoning, tasks where you need tokens moving fast and reliably, and where E2E throughput is the bottleneck, not TTFT. In our head-to-head benchmarks, it delivers 81-113 tokens per second, roughly 2x the throughput of the next closest provider on the same model.
Best for: Function calling, high-throughput reasoning, batch and agentic workloads where E2E latency and throughput are the priority. Not recommended for real-time voice.
MiniMax-M2.7 is the value play. On our benchmarks, it runs 3-6x faster on Telnyx than on competing providers.
This is the model that proves the efficient frontier concept. It delivers high intelligence at a fraction of the cost of models that score similarly. If you're running high-volume production inference and cost-per-token matters, MiniMax-M2.7 is the answer.
Best for: High-volume production deployments, cost-sensitive workloads, any scenario where intelligence-per-dollar is the primary metric.
Qwen3-235B-A22B uses a mixture-of-experts architecture with 235B total parameters but only 22B active per token. That MoE design means you get near-frontier intelligence at a fraction of the compute cost, it sits on the efficient frontier as our best option for balanced workloads where you need strong reasoning without the price tag of running a dense 200B+ model.
MoE activation keeps costs low while output quality stays high, and on Telnyx infrastructure, that efficiency compounds with our throughput advantage.
Best for: Balanced workloads, MoE efficiency, strong reasoning at moderate cost.
Every model on Telnyx Inference is open-weight. That's not a side note, it's the whole point.
When you build on proprietary APIs, you're building on someone else's infrastructure with someone else's switching costs. Your prompts, your agents, and your workflows depend on a model you can't run anywhere else. The provider can change pricing, deprecate the model, or alter behavior, and your only option is to re-engineer everything.
Open weights mean you can take your workloads anywhere. We earn your inference business on performance and flexibility, not on the cost of leaving.
We'd rather add one model that shifts the frontier than ten that don't. When a new model lands above the line, we add it. When something better arrives at the same price point, the old one goes.
Still not sure which model to use? Here's the decision framework:
| If you need... | Use... | Because... |
|---|---|---|
| Voice AI and real-time responses | Kimi K2.6 | Lowest TTFT on our platform, non-reasoning mode stays intelligent |
| High-throughput reasoning and function calling | GLM-5.1-FP8 | 2x throughput vs competitors, best for batch and agentic workloads |
| Best intelligence-per-dollar | MiniMax-M2.7 | 3-6x faster than competitors, highest throughput per dollar |
| MoE efficiency for balanced workloads | Qwen3-235B-A22B | 235B total / 22B active params, strong reasoning at low compute cost |
You don't have to pick just one. Most production systems route different tasks to different models. A voice AI pipeline might use Kimi K2.6 for real-time responses and GLM-5.1 for complex follow-up analysis. An autonomous agent might use GLM-5.1 for the main reasoning loop and MiniMax-M2.7 for high-volume sub-tasks.
The efficient frontier isn't about finding one model that does everything. It's about only using models that are the best at something.
Model count is a vanity metric. What matters is whether every model you're paying for sits on the efficient frontier, the line where you get the most intelligence for the cost.
Every model we host on our dedicated infrastructure is open-weight. Every one is best-in-class at something. No filler, no legacy, no lock-in.
Try Telnyx Inference — All five models, serverless, with regional availability in the US, EU, and APAC. Sign up and start building, or talk to our team about production workloads.
