GLM-5 (Reasoning)

GLM-5 (Reasoning) is a credible shortlist candidate for developers who value broad benchmark placement more than proven production behavior. The model scores 39.5 on the Artificial Analysis Intelligence Index and ranks 68 of 578 evaluated models, placing it within the stronger part of a large comparison set. The source data identifies a 0.3-second latency figure, but it does not report median output tokens per second. That distinction matters for interactive applications because first-token responsiveness and sustained generation speed affect different parts of the user experience.\n\nThe available evidence is narrow. The research brief contains no verified official positioning, pricing statement, community commentary, or documented failure scenarios. Developers should therefore treat this review as a selection analysis based on the supplied benchmark and commercial data, rather than as a complete operational profile. All numerical evidence comes from Artificial Analysis.

GLM-5 (Reasoning) offers a defensible balance of benchmark strength and cost, but its production fit remains unproven. Its Intelligence Index score of 39.5 is close to the supplied neighboring models, which range from 39.1 to 40, so GLM-5 does not establish a clear quality lead from this metric alone. Its $1.55 blended-token price is materially below Gemini 3 Pro Preview (high) at $4.50 and GPT-5.4 (low) at $5.625, while remaining above Qwen3.6 Plus at $1.125 and below GPT-5.4 mini (xhigh) at $1.6875.\n\nThat position makes GLM-5 most interesting when a team wants a reasoning-oriented model without paying the highest neighboring prices. The case weakens when coding performance, sustained generation throughput, or independently documented failure behavior is central to the decision.\n\n| Decision factor | What GLM-5 suggests | Selection implication |\n|—|—|—|\n| Broad benchmark standing | Upper-tier placement in the supplied ranking | Suitable for an initial shortlist |\n| Relative quality | Near the neighboring models on the Intelligence Index | Do not assume a decisive quality advantage |\n| Cost | Mid-range among the closest models | Attractive for controlled workloads |\n| Operational evidence | Output speed and failure evidence are incomplete | Require task-level validation before rollout |\n\nArtificial Analysis provides the supplied ranking and pricing snapshot.

GLM-5 (Reasoning) is better viewed as a competitive general reasoning option than as a proven leader for every developer workload. Ranking 68 of 578 indicates that the model sits well above the middle of the supplied evaluation pool, while its 39.5 Intelligence Index score remains close to the neighboring scores. That combination supports confidence that GLM-5 deserves testing, but it does not identify the exact tasks where it will outperform alternatives.\n\nFor product teams, the practical meaning is conditional. GLM-5 may be suitable for structured analysis, multi-step assistance, and workflows where answer quality matters more than maximum generation throughput. The word “may” is important because the research brief provides no task examples, user reports, or documented failure patterns. The supplied data also does not include a GLM-5 coding score. Qwen3.6 Plus has a coding score of 54.5, Grok Build 0.1 0616 has 51.5, and GPT-5.4 mini (xhigh) has 56.1, but those figures do not establish GLM-5’s coding behavior.\n\nThe 0.3-second latency figure supports a reasonable first-token experience in the supplied snapshot. It says nothing about how quickly the model completes long answers because median output tokens per second is not reported. A chat interface may feel responsive at the start yet still feel slow during extended reasoning. Teams should test both time to first token and completion duration using representative prompts.\n\nThe central performance conclusion is therefore bounded: GLM-5 has enough ranking evidence to justify evaluation, but not enough evidence to justify assuming task-specific superiority.

GLM-5 (Reasoning) becomes a weaker choice when sustained throughput, coding evidence, or failure transparency matters more than broad benchmark placement. The supplied benchmark does not expose the dimensions behind the Intelligence Index score, so developers cannot infer whether the ranking reflects reasoning, coding, factuality, instruction following, or another mixture of capabilities. A single aggregate score can support screening, but it cannot replace workload-specific tests.\n\nThe comparison set shows why this matters. Gemini 3 Pro Preview (high) has an Intelligence Index score of 39.6 and a math score of 95.7. Qwen3.6 Plus also scores 39.6 on the Intelligence Index and has a coding score of 54.5. Grok Build 0.1 0616 scores 39.8 and has a coding score of 51.5. GPT-5.4 mini (xhigh) reaches 40 and has a coding score of 56.1. These adjacent figures show that similar broad scores can coexist with different specialized evidence.\n\nGLM-5 should not be selected for software engineering, mathematical problem solving, or high-volume generation solely because it ranks 68 of 578. The research brief offers no verified sources describing its strengths or failure cases. Evidence is insufficient to state that GLM-5 handles any of those workloads better than the neighboring models. The responsible path is a small evaluation set covering correctness, instruction adherence, refusal behavior, latency, and output length before committing to an integration.

GLM-5 (Reasoning) is priced as a middle-ground option, and that makes its value depend on whether its reasoning quality reduces downstream work. The blended-token price is $1.55 per 1M tokens, with input priced at $1 and output priced at $3. The output rate is therefore the more important cost driver for verbose reasoning workflows, while input-heavy applications may experience a different effective cost profile.\n\nAgainst the closest supplied models, GLM-5 costs less than Gemini 3 Pro Preview (high) at $4.50, GPT-5.4 (low) at $5.625, and GPT-5.4 mini (xhigh) at $1.6875. It costs more than Qwen3.6 Plus at $1.125 and Grok Build 0.1 0616 at $1.25. This places GLM-5 between lower-cost alternatives and substantially more expensive options.\n\nThe price is attractive when the model’s answers are accurate enough to reduce retries, manual review, or multi-model routing. The price is less attractive when the application needs high output volume and GLM-5’s unreported generation speed creates queues or longer sessions. Cost per token alone cannot establish value because the brief does not provide quality-per-dollar measurements, throughput data, or failure rates.\n\n| Cost position | Practical reading |\n|—|—|\n| Below $1.6875 neighboring price | Competitive for teams avoiding premium pricing |\n| Above $1.125 and $1.25 alternatives | Not the lowest-cost option in the supplied set |\n| $3 output price | Important for long generated responses |\n| Missing throughput evidence | Prevents a complete cost-per-completed-task judgment |\n\nThe pricing snapshot comes from Artificial Analysis, while workload economics still require testing with actual prompt and response distributions.

GLM-5 (Reasoning) is worth its price when moderate token cost and credible broad performance matter more than the lowest bill or strongest specialized evidence. A team building an internal assistant, research workflow, or reasoning-heavy prototype may prefer GLM-5 because its $1.55 blended rate avoids the premium attached to several neighboring models. The decision is strongest when requests are valuable enough that answer quality matters, yet frequent enough that the higher-priced alternatives would materially affect budget.\n\nThe case is weaker for simple extraction, classification, or short transformation tasks. Those workloads may not benefit from a reasoning-oriented model, and the supplied data does not show that GLM-5 delivers better task outcomes than cheaper alternatives. The case is also weaker for applications that generate long outputs at high volume. GLM-5’s output price is $3 per 1M tokens, and sustained output speed is not reported.\n\nDevelopers should calculate value using completed tasks rather than token price alone. Track accepted answers, retries, human corrections, latency, and output length during a controlled trial. The available evidence does not include any of those operational measures, so claims about return on spend would be speculative. GLM-5 is a reasonable candidate for a measured pilot, not a model that the supplied data alone can validate for production.

GLM-5 (Reasoning) should enter a developer’s shortlist when broad reasoning performance and moderate pricing are the primary requirements. Its rank of 68 of 578 and Intelligence Index score of 39.5 provide enough evidence for serious evaluation. Its position near models scoring 39.1, 39.6, 39.8, and 40 also argues against treating it as an obvious winner.\n\nChoose GLM-5 first for a pilot if your workload contains multi-step requests, requires a responsive first token, and can tolerate incomplete evidence about sustained speed. Compare it with Qwen3.6 Plus when price and coding evidence are important. Compare it with GPT-5.4 mini (xhigh) when a slightly higher blended price is acceptable and coding evidence is relevant. Consider Gemini 3 Pro Preview (high) or GPT-5.4 (low) only when their broader product or task-specific advantages are verified separately, because the supplied brief does not provide those qualitative details.\n\nDo not make GLM-5 the default production model solely from this snapshot. The research brief has no official source, community source, or verified failure analysis. The missing median output speed also limits capacity planning. Run representative prompts before adoption, then keep a fallback path until error rates and completion times are known.\n\n| Use case | Recommendation | Reason |\n|—|—|—|\n| Reasoning-heavy pilot | Recommend testing GLM-5 | Strong ranking with moderate cost |\n| Lowest-cost routing | Compare before choosing | Qwen3.6 Plus and Grok Build 0.1 0616 are cheaper |\n| Coding-first workflow | Require direct evaluation | GLM-5 coding evidence is absent |\n| High-volume long outputs | Require throughput testing | Median output speed is not reported |\n| Production default | Wait for task evidence | Failure and community evidence are unavailable |

GLM-5 (Reasoning) is a plausible evaluation candidate, but the supplied evidence supports a pilot more strongly than an immediate production decision. The following answers separate what the data shows from what remains unknown.\n\nData provided by https://artificialanalysis.ai/