DeepSeek V4.1 Flash vs GLM-5.2: Two MIT Models, One Boring Answer

DeepSeek V4.1 Flash and GLM-5.2 are the same kind of object, and that is the part most comparisons skip. Both are mixture-of-experts models, both ship under MIT with downloadable weights, both take a million tokens of context, and both are available through a hosted API from a vendor that did not train them. GLM-5.2 arrived first and was, at its release, the highest-scoring open-weight model on the Artificial Analysis Index at 51. DeepSeek V4.1 Flash arrived on 10 September 2026 as the smaller, newer, cheaper model in DeepSeek's new architecture family. The comparison that matters between them is not which is smarter. It is which one you can run, and at what cost, for the work you actually have.

What they share, which is most of it

Start with the overlap, because it eliminates most of the usual decision criteria. If you are choosing between an open model and a closed one, you weigh lock-in, you weigh the ability to fine-tune, you weigh whether the vendor can change terms on you. None of that applies here. Both DeepSeek V4.1 Flash and GLM-5.2 are MIT-licensed with weights you can pull and serve yourself. Both take 1M tokens of input. Both are MoE architectures, which means both are cheap to run relative to their total parameter count, because only a fraction of the weights activate per token.

So the comparison is not open versus closed, and it is not long-context versus short. It is a set of four or five numbers, and the numbers point in one direction on cost and the other direction on maturity.

Where they actually diverge

• Price per 1M tokens — DeepSeek V4.1 Flash $0.15 in / $0.60 out off-peak vs GLM-5.2 $1.40 in / $4.40 out. That is just over 9 times the input price and just over 7 times the output price.

• Cached input — V4.1 Flash $0.003 per 1M off-peak vs GLM-5.2 $0.26 per 1M. Nearly 90 times, on the line item that dominates an agent loop's bill.

• Parameters — V4.1 Flash 552B total with 8B active on input and 16B on output vs GLM-5.2 roughly 745B total with around 40B active. GLM-5.2 activates about two and a half times as many parameters per token.

• Maximum output — V4.1 Flash 384K tokens vs GLM-5.2 128K tokens. Three times the ceiling.

• Context window — 1M tokens each. Level.

• Independent index score — GLM-5.2 scores 51 on the Artificial Analysis Index, the highest of any open-weight model at its release. DeepSeek V4.1 Flash does not yet have a published Index figure.

• Observed latency to first token — V4.1 Flash 2.63 s at p50 and 9.05 s at p95 vs GLM-5.2 2.36 s at p50 and 10.00 s at p95, on OrcaRouter's own 7-day telemetry. The medians are level. The tails are not.

The price direction and the maturity direction are opposite. GLM-5.2 is the model with the independent score and the longer track record. DeepSeek V4.1 Flash is the model with the cheaper tokens, a ninth of the input price and three times the output ceiling. There is no reading of this list where one model simply dominates.

The tail is the underrated line

Most comparisons of open-weight models lead with the index score. The latency telemetry deserves attention instead, but not for the reason you would expect: the medians are level. GLM-5.2's p50 time to first token is 2.36 s against V4.1 Flash's 2.63 s, which is not a gap, it is noise on a shared network. Anyone quoting a first-token advantage for the cheaper model is reading the wrong percentile.

The p95 is where the two separate, and the direction is the reverse of what the price gap would suggest. GLM-5.2's worst-case wait is 10.00 s; V4.1 Flash's is 9.05 s. That matters more than a median does, because the requests a user notices are the slow ones. A tool that is usually instant and occasionally stalls for ten seconds reads as unreliable in a way that a uniformly three-second tool does not, and in an agent loop that fans out ten calls per turn, the p95 is the number that decides whether the turn completes inside a person's patience. GLM-5.2's tail is not a defect; it is a larger model activating roughly 40 billion parameters per token and it costs what it costs. But it is the reason the model fits asynchronous work — a queue, a batch job, a background agent nobody is watching — better than it fits a request-response interface.

Neither model publishes a throughput figure on the pages we can see, which is worth saying plainly rather than filling in. Time to first token is the only latency dimension on which these two can be compared on common data, and on that dimension the honest reading is that they are level at the median and close at the tail.

What an index score does and does not settle

GLM-5.2's 51 on the Artificial Analysis Index is a real, independently produced figure and it is the strongest single argument for the model. It is also a composite: a single number aggregating several evaluation sets, which makes it a good summary of general capability and a poor predictor of performance on any one specific task. A model that scores 51 and a model with no published score are not the same as a model that scores 51 and a model that scores 40.

The honest position on DeepSeek V4.1 Flash is that its independent record is thin, and the reason is age. It has been generally available for twelve days. The one recent third-party sweep it appears in — the Agents on Rails agentic-coding board published on 21 September 2026 — placed it at 17% on maximum effort, mid-field, above GLM-5.3 Flash at 15% and below Gemini 3.8 Flash at 23%. That is a single board measuring one narrow thing, and it is not a substitute for an Index score. Anyone claiming V4.1 Flash beats GLM-5.2 on capability right now is extrapolating from architecture and price, not reporting a measurement.

The case for each, stated plainly

DeepSeek V4.1 Flash is the model to reach for when the workload is high-volume, latency-sensitive, or output-heavy. A ninth of the input price and roughly a ninetieth of the cache-read price is a structural advantage in an agent loop that re-reads context every turn, and a 384K output ceiling is a different category of artefact than 128K. If your task is classification, extraction, long structured generation, or the high-volume executor inside an agent pipeline, the cost difference is not marginal — it is the difference between a viable pipeline and an unviable one.

GLM-5.2 is the model to reach for when you need a published, independently verified capability figure to justify a decision, or when the work is asynchronous and a ten-second worst-case wait is invisible. It is the mature choice: the score exists, the behaviour is documented, the model has been in production long enough for other people to have found its edges. There is real value in that, and it is not captured by a price column.

Where neither is obviously right — a general-purpose assistant handling mixed traffic — the useful move is not to pick. It is to send the bulk of the traffic to the cheap model and hold the expensive one for the requests that need it.

Running both as one system

Because both models are open-weight and both are hosted, the split-and-route pattern is unusually cheap to set up here, and it is where OrcaRouter's routing layer earns its place rather than being a bolted-on pitch. Both models sit behind a single key, so the routing decision is configuration rather than a second integration: a rule that sends short, high-volume requests to DeepSeek V4.1 Flash and long asynchronous jobs to GLM-5.2 is a few lines rather than a branch in your application code.

Two properties of the platform matter specifically for this pairing. OrcaRouter passes provider list pricing through at 0% markup, so the figures above are the vendors' own rates and the DeepSeek peak schedule applies exactly as DeepSeek defines it — peak is 01:00–04:00 and 06:00–10:00 UTC on weekdays, with weekends entirely off-peak, which is a scheduling decision worth making explicitly on a model this cheap. And the routing DSL can compose several models into one call, which is the natural way to use two open-weight models whose strengths do not overlap: the cheap one produces, the stronger one reviews, and the caller sees a single response. Automatic failover sits behind both paths, which matters when one of the two models is twelve days old and its behaviour on your data is not yet known.

The reason this is the right shape rather than a compromise is that the two models differ on axes that do not compete. One is fast and cheap; the other is scored and slow. A pipeline that uses both is not hedging, it is matching instruments to tasks.

What to watch

• A published Artificial Analysis Index figure for DeepSeek V4.1 Flash. Until it exists, the capability comparison between these two models is an argument, not a measurement — and it is the single piece of evidence that would settle it.

• Whether GLM-5.2's latency profile improves. Its p95 of 10.00 s is the number most likely to move as hosting providers optimise, and it is currently the largest single measured difference between the two models — a tail wait, not a price.

• Whether DeepSeek's peak-hour schedule changes. On a model at $0.15 per million input tokens, the peak multiplier is the largest single variable in the cost model.

Until the first of those lands, the accurate summary is: DeepSeek V4.1 Flash is roughly nine times cheaper on input and nearly ninety times cheaper on cached input than GLM-5.2, and it has three times the output ceiling; GLM-5.2 is the model with the independent score and the longer track record, and its first-token median is level with the cheaper model's even though its worst case is not. Both are MIT. Both are one key away. Pick by workload, not by winner.