GLM-5.1 (Non-reasoning) vs o3: The Ultimate Performance & Pricing Comparison

GLM-5.1 (Non-reasoning) is the stronger default on the supplied intelligence and pricing data, but o3 remains the only model with a reported mathematics score and output-speed figure. The Artificial Analysis snapshot gives GLM-5.1 (Non-reasoning) an Intelligence Index of 35.4, compared with 30.4 for o3. It also lists a blended price of $2.135 per 1M tokens for GLM-5.1 (Non-reasoning), compared with $3.5 for o3. The same snapshot reports o3 at 128.056 median output tokens per second, while GLM-5.1 (Non-reasoning) has no reported output-speed value. Both models show 0.3 seconds of latency in the supplied data.

The comparison has an important evidence boundary. The research brief contains no verified official positioning, API status, context window, failure modes, or community testing for GLM-5.1 (Non-reasoning). For o3, the supplied OpenAI materials do not list the model in the current directory or pricing page. The data attribution is Data provided by https://artificialanalysis.ai/.

GLM-5.1 (Non-reasoning) is the practical first choice for cost-sensitive general workloads because its measured intelligence score is higher and its listed token prices are lower. That recommendation is conditional, because the supplied research does not establish whether GLM-5.1 (Non-reasoning) is currently callable, has a stable alias, or offers dependable production documentation.

The broad score should guide ordinary text, classification, extraction, and assistant workloads only as a directional signal. It does not prove superiority for every task. The mathematics score should guide engineering, symbolic reasoning, and difficult quantitative workflows only with the same caution, because no matching GLM-5.1 value is supplied. Developers should treat availability as a gating question, not a minor implementation detail. OpenAI's current model directory presents a different current product lineup and does not include o3: OpenAI Models.

o3 is the safer specialist choice for mathematics because its supplied Math Index is 88.3, while GLM-5.1 (Non-reasoning) has no reported mathematics value. That difference does not establish that o3 is better at every reasoning task. It establishes only that the available evidence is more specific for o3 in mathematics.

GLM-5.1 (Non-reasoning) leads the supplied Intelligence Index at 35.4 versus o3 at 30.4. For a developer selecting a general-purpose model, that result supports GLM-5.1 as the initial candidate for broad task coverage. The result cannot answer whether it writes better code, follows structured output constraints more reliably, or handles long prompts more consistently. The research brief contains no verified test reports for those behaviors.

o3 is the only model with a reported median output speed, at 128.056 output tokens per second. GLM-5.1 (Non-reasoning) has no reported value, so the evidence cannot prove that o3 is faster than GLM-5.1. Both models have a reported latency of 0.3 seconds, which suggests no latency advantage in this snapshot. It does not reveal time-to-first-token behavior, streaming smoothness, queueing effects, or performance under concurrency.

The most useful production interpretation is therefore asymmetric. Choose o3 when the application has a math-heavy acceptance test and can validate access. Choose GLM-5.1 when broad measured capability and lower listed price matter, but first confirm endpoint access and repeatable speed in your own environment.

GLM-5.1 (Non-reasoning) is the lower-cost option on every supplied token-price field, but availability uncertainty can make an apparently cheaper model more expensive to operate. Its blended price is $2.135 per 1M tokens, compared with $3.5 for o3. Its input price is $1.38 versus $2, and its output price is $4.4 versus $8.

The chart below this section already shows those price differences, so the key question is how workload shape changes their practical meaning. Output-heavy applications should pay close attention to the output price field because generated tokens are priced differently from input tokens. Long-context applications should inspect input volume, caching behavior, and prompt repetition before trusting a blended estimate. The supplied research does not provide context-window values or caching terms for either model, so those cost drivers remain unresolved.

The cheaper model can also become the more expensive choice if it cannot be called reliably, lacks a stable alias, or requires additional routing and fallback engineering. Those are not confirmed GLM-5.1 failures. They are unanswered deployment questions. The same uncertainty applies to o3 from a different direction: the supplied OpenAI pricing page does not list an o3 price, even though the Artificial Analysis snapshot supplies one. The official pricing source is OpenAI API Pricing.

For budgeting, use the supplied blended prices as comparison inputs, not as a complete operating-cost forecast. Confirm current vendor billing, access, rate limits, and retry behavior before committing to either model.

GLM-5.1 (Non-reasoning) is the recommended starting point for general developer workloads when the model is verifiably available and broad capability per dollar is the priority. Its supplied Intelligence Index is 35.4, and its blended price is $2.135 per 1M tokens. That combination makes it the more attractive first experiment for assistants, extraction, classification, content transformation, and other workloads where mathematics is not the sole acceptance criterion.

o3 is the recommended candidate for math-centered workflows when its reported 88.3 Math Index reflects the application's real failure modes. Examples include quantitative analysis, symbolic manipulation, and code tasks that depend on mathematical correctness. The recommendation remains conditional because the supplied official OpenAI model directory does not list o3, and the research brief does not verify a current endpoint, stable alias, or replacement relationship. See the OpenAI model directory.

A sensible selection sequence is simple:

1. Test GLM-5.1 (Non-reasoning) against the broad task set because it leads the supplied Intelligence Index and has the lower listed prices.
2. Test o3 on the math-heavy subset because it is the only model with a supplied Math Index of 88.3.
3. Reject any candidate that fails access, stability, or application-specific quality checks, regardless of its benchmark position.

The final choice should therefore be workload-specific. GLM-5.1 is the value-oriented default on current data. o3 is the evidence-backed mathematics specialist. Neither model has enough supplied deployment evidence to justify an unconditional production recommendation.

o3 is the model with the clearer documented absence problem, while GLM-5.1 (Non-reasoning) has a broader but less specific evidence gap. The supplied OpenAI research says the current model directory lists GPT-5.6 Sol, GPT-5.6 Terra, and GPT-5.6 Luna as the latest frontier models, but does not list o3. It also does not explain whether o3 remains callable, has a stable alias, or was formally replaced. The relevant source is OpenAI Models.

GLM-5.1 (Non-reasoning) has no supplied official positioning, pricing page, API documentation, alias information, or community evidence. That absence is not proof of unavailability. It means the research brief cannot support a confident operational claim. Developers should verify the exact model identifier, provider endpoint, region support, rate limits, and deprecation policy before building around it.

This difference matters during procurement and migration. A benchmark snapshot can identify a promising candidate, but a production model also needs an addressable API and a stable contract. The supplied materials do not establish those contracts for either model. Availability testing should happen before extensive prompt tuning or application integration.

GLM-5.1 (Non-reasoning) cannot be judged on several production behaviors because the supplied research contains no verified qualitative evidence, while o3 cannot be judged on those behaviors either from the available official pages. The briefs provide no reliable community tests for coding experience, speed perception, instruction following, structured output, refusal behavior, or known failure scenarios.

The supplied data also leaves both context windows as null. That prevents a defensible conclusion about long-document tasks, retrieval-heavy prompts, or conversation memory. It also reports no output-speed figure for GLM-5.1, so the presence of a reported 128.056 value for o3 should not be converted into a complete speed ranking.

The fairest conclusion is narrower than a typical model comparison. GLM-5.1 has the higher supplied broad intelligence score and lower supplied prices. o3 has the only supplied mathematics score and output-speed figure. The evidence is insufficient to determine which model produces better code, handles longer context, fails less often, or offers the more stable API. Those questions require task-level evaluation and current endpoint verification.

GLM-5.1 (Non-reasoning) is the value-oriented default, while o3 is the more evidence-supported candidate for mathematics, so the correct choice depends on workload and verified availability. The supplied research does not justify treating either model as universally superior.

Developers should use the benchmark data to define a shortlist, then validate the missing operational facts with live access and representative tests. The questions below focus on the decisions that the supplied briefs leave partially unresolved.