GLM-5.2 (Non-reasoning)

GLM-5.2 (Non-reasoning) is a fast, mid-table model whose strongest case is practical throughput, not clearly established frontier quality or vendor support.\n\nThe available evaluation places GLM-5.2 (Non-reasoning) at 106 of 578 models on the Artificial Analysis Intelligence Index, with a score of 34.1. Its coding position is stronger, at 80 of 202 models with a score of 46.5. Those rankings suggest a model that may serve routine developer workloads, while leaving substantial uncertainty about difficult reasoning, reliability, and production integration.\n\nThe data source reports 128.519 median output tokens per second and 0.3 seconds to first token. That combination makes responsiveness a meaningful part of the selection case. The same source reports a blended price of $2.07575 per 1M tokens.\n\nPublic research found no verifiable official release announcement, developer documentation, pricing page, or reliable community testing for this model. The benchmark data is therefore useful for relative positioning, but it cannot establish a complete production profile.\n\nData provided by https://artificialanalysis.ai/.

GLM-5.2 (Non-reasoning) is worth testing for high-volume, latency-sensitive tasks, but the evidence is too incomplete for an unqualified production recommendation.\n\nThe model sits well above the median implied by its ranking position on the intelligence evaluation, and its coding ranking is comparatively better. That combination points toward a reasonable shortlist candidate for code assistance, structured transformations, classification, drafting, and other workloads where response time matters. It does not prove that GLM-5.2 (Non-reasoning) will outperform a neighboring model on your data.\n\nThe closest available reference points show three different trade-offs. GPT-5.6 Terra (Non-reasoning) has a similar intelligence score and a higher coding score, but its blended price is more than twice as high. Qwen3.5 27B (Reasoning) has a slightly lower intelligence score and a lower blended price, though the supplied data does not provide comparable speed. GLM-4.7 (Reasoning) has a lower intelligence score but a coding score close to GLM-5.2 (Non-reasoning), plus a much lower blended price.\n\n| Decision factor | GLM-5.2 (Non-reasoning) | Nearby alternative | Practical reading |\n|—|—|—|—|\n| General quality | Higher than GLM-4.7 and Qwen3.5 27B in the supplied intelligence ranking | GPT-5.6 Terra is nearly tied | Test task-level quality before switching |\n| Coding position | Better than GLM-4.7 in the supplied coding ranking | GPT-5.6 Terra scores higher | A promising coding candidate, not a guarantee |\n| Responsiveness | Strong measured output speed | Claude Sonnet 4.6 is slower in the supplied data | Useful for interactive tools |\n| Verification risk | No confirmed public product documentation | Established alternatives have clearer identities in the data | Treat availability as a launch gate |\n\nThese comparisons use the benchmark and pricing snapshot at Artificial Analysis.

GLM-5.2 (Non-reasoning) looks more useful for routine coding and interactive workloads than for tasks requiring dependable frontier-level reasoning.\n\nThe coding ranking is the clearest positive signal. GLM-5.2 (Non-reasoning) ranks 80 of 202 on the Artificial Analysis Coding Index, with a score of 46.5. That position supports testing for code completion, bug explanation, test generation, refactoring suggestions, and repository questions with bounded scope. It does not show whether the model follows local conventions, preserves APIs, or produces safe patches. Those questions require a task-specific evaluation.\n\nThe broader intelligence ranking is weaker in relative terms. GLM-5.2 (Non-reasoning) ranks 106 of 578 at 34.1. This is a solid shortlist signal, but not evidence that the model should handle complex planning, ambiguous requirements, or high-cost autonomous actions without review. The supplied research contains no reliable description of failure modes, coding habits, tool use, or community experience. Evidence is therefore insufficient for claims about hallucination rates, instruction following, or long-context behavior.\n\nSpeed changes the practical interpretation. The model records 128.519 median output tokens per second and 0.3 seconds to first token in the supplied data. For an IDE assistant, chat interface, or streaming API, fast generation can improve perceived responsiveness. For batch jobs, speed matters less than error correction and output acceptance rate. A faster model becomes a poor choice if developers must spend more time checking or repairing its work.\n\nThe non-reasoning label also matters. The available snapshot does not explain how that mode differs from other GLM variants, what reasoning is disabled, or how the setting affects difficult coding tasks. Developers should benchmark both simple and multi-step tasks before treating the coding rank as a general capability claim. The ranking evidence comes from Artificial Analysis.

GLM-5.2 (Non-reasoning) offers a plausible cost-performance balance, but its price advantage only matters if its outputs reduce total engineering effort.\n\nThe supplied blended price is $2.07575 per 1M tokens. That places the model below GPT-5.6 Terra (Non-reasoning) at $4.500000000000001 and Claude Sonnet 4.6 (Non-reasoning, Low Effort) at $6. It is above Qwen3.5 27B (Reasoning) at $0.825 and GLM-4.7 (Reasoning) at $1. In other words, GLM-5.2 (Non-reasoning) is positioned as a middle-cost option among the provided references.\n\nThat middle position is attractive for interactive applications that need better measured coding performance than the cheapest reference while avoiding the highest-priced alternatives. It is less compelling for very large batch workloads, where Qwen3.5 27B or GLM-4.7 may offer lower token spend if their quality is adequate. It is also less compelling for high-risk tasks if a more expensive model materially improves first-pass correctness.\n\nThe price should not be evaluated separately from speed. GLM-5.2 (Non-reasoning) records 128.519 median output tokens per second, while Claude Sonnet 4.6 records 56.53 in the supplied snapshot. For a user-facing stream, that difference may reduce waiting time. For a batch pipeline, it may have little value unless it improves throughput at the system level.\n\nThe pricing evidence has an important limitation. Research found no verifiable public pricing page or confirmed provider endpoint for GLM-5.2 (Non-reasoning). The snapshot can support comparison, but it cannot confirm whether the quoted price is currently available, stable, or accessible through a dependable API. Validate the actual invoice rate, quotas, rate limits, and model identifier before committing. The underlying data is reported by Artificial Analysis.

GLM-5.2 (Non-reasoning) deserves a controlled pilot for fast coding assistance and general developer automation, but it should not become a default dependency before basic product facts are verified.\n\nChoose GLM-5.2 (Non-reasoning) when three conditions hold. First, your workload benefits from fast streaming responses. Second, your quality target is strong routine assistance rather than proven frontier reasoning. Third, your team can run a representative evaluation and tolerate uncertainty around provider continuity. The ranking and speed data support this limited recommendation.\n\nKeep GPT-5.6 Terra (Non-reasoning) in the comparison set when coding correctness matters more than token cost. Keep Qwen3.5 27B and GLM-4.7 in the comparison set when token economics dominate and your workload can absorb additional validation. These are reference choices from the supplied snapshot, not claims that any alternative will perform better on your application.\n\nA sensible pilot should measure accepted code changes, test pass rate, correction turns, latency under load, and failure severity. The supplied material does not provide those application-level results, so no reliable conclusion is available about production reliability. It also does not confirm context-window size, output limits, multimodal support, API parameters, stable aliases, current availability, or whether a later version has replaced the model.\n\nThe final decision should therefore be conditional: approve GLM-5.2 (Non-reasoning) for a bounded experiment, then promote it only if live endpoint checks and task-level tests confirm the benchmark story. The relative benchmark and price snapshot is available from Artificial Analysis.

GLM-5.2 (Non-reasoning) is best treated as a promising but incompletely documented candidate for developer workloads.\n\nThe benchmark snapshot supports a focused pilot, especially where speed and moderate token cost matter. The research brief does not support confident claims about deployment mechanics, model behavior, or long-term availability. Developers should resolve those unknowns before exposing the model to sensitive data or autonomous production actions.\n\nThe answers below separate what the supplied data supports from what remains unverified.