o3 vs Qwen3.5 397B A17B (Reasoning): The Ultimate Performance & Pricing Comparison

Qwen3.5 397B A17B (Reasoning) is the stronger default on the available intelligence score, while o3 is faster and has the only supplied math score.

The choice is not a simple capability ranking. The supplied data gives Qwen3.5 397B A17B (Reasoning) an Artificial Analysis Intelligence Index of 33.7, compared with o3 at 30.4. It also gives Qwen3.5 397B A17B (Reasoning) a coding index of 48.2, while no corresponding o3 coding value is supplied. o3 has a math index of 88.3, while no corresponding Qwen3.5 math value is supplied.

The operational picture is equally divided. o3 produces a median 128.056 output tokens per second, exactly twice the practical speed of Qwen3.5 397B A17B (Reasoning) in the supplied snapshot. Both models show 0.3 seconds of latency. Qwen3.5 397B A17B (Reasoning) costs $1.35 per 1M blended tokens, while o3 costs $3.5.

The largest risk is not a missing benchmark point. It is deployment uncertainty. The supplied research does not identify a reliable official product page, stable API alias, or current price for Qwen3.5 397B A17B (Reasoning). The current OpenAI model directory also does not list o3 among the latest models, and the OpenAI pricing page does not list an o3 price. Treat the comparison as a data-backed model choice, not as confirmation that either name is currently callable.

Qwen3.5 397B A17B (Reasoning) is the better value and the stronger measured general-intelligence option, but o3 remains attractive for latency-sensitive reasoning workflows.

The score advantage does not prove that Qwen3.5 397B A17B (Reasoning) wins every developer task. The models do not have matching coverage across every evaluation. A buyer cannot directly compare the supplied 48.2 coding score with o3's 88.3 math score because they measure different categories.

The price advantage is more actionable for high-volume applications, provided the listed price corresponds to the endpoint a team can actually access. The speed advantage is more actionable for interactive applications, provided o3 remains available under a supported API name.

The official evidence is asymmetric. OpenAI publishes a current model directory and pricing page, but the supplied pages omit the o3 details that a new integration normally needs. The Qwen3.5 research contains no reliable official or community source. Those gaps lower confidence in procurement, migration planning, and long-term maintenance.

o3 is the better fit for response-speed-sensitive interfaces, while Qwen3.5 397B A17B (Reasoning) has broader measured evidence in the supplied general and coding fields.

The output-speed gap changes user experience. o3's median output rate is 128.056 tokens per second, compared with 64.156 for Qwen3.5 397B A17B (Reasoning). In a streamed coding assistant, fast output can make edits feel more immediate. In an agent that spends most of its time waiting on tools, databases, or approval steps, that advantage may matter less than task accuracy.

The equal 0.3-second latency values create a useful distinction. The models start responding at the same supplied latency, but o3 then emits generated text faster. A user may therefore see similar time to first response while experiencing a shorter completion with o3. The snapshot does not provide end-to-end completion times, token counts per task, tool-call latency, or reliability data, so developers should not convert the speed result into a guaranteed application-level response time.

Qwen3.5 397B A17B (Reasoning) has the higher supplied Artificial Analysis Intelligence Index, at 33.7 versus o3 at 30.4. It also has a coding index of 48.2. That combination supports testing Qwen3.5 for code generation, code explanation, and general reasoning workflows. It does not establish a direct coding win because no o3 coding result appears in the snapshot.

o3's math index of 88.3 is a meaningful reason to test it for quantitative reasoning. It does not establish a direct math win because no Qwen3.5 math result appears. The supplied research also contains no verified task-level failure analysis, community testing methodology, or official benchmark report for either model. The evidence supports targeted pilots, not universal claims.

Qwen3.5 397B A17B (Reasoning) has the lower listed token price, but o3 may be cheaper for workflows where faster completion reduces user waiting or infrastructure time.

The supplied blended price is $1.35 per 1M tokens for Qwen3.5 397B A17B (Reasoning), compared with $3.5 for o3. The input prices are $0.6 and $2 respectively, while output prices are $3.6 and $8 respectively. The cost gap therefore matters most in applications that generate substantial output, such as code repair, long explanations, multi-step planning, and agent traces.

A lower token price does not automatically produce a lower product cost. Developers should include retries, rejected outputs, tool calls, human review, cache behavior, and task completion rates in a pilot. The supplied data does not provide any of those production variables. It also does not confirm whether the Qwen3.5 price is a current public API price, a provider-specific price, or an accessible deployment price.

The same caution applies to o3. The OpenAI API pricing page does not list o3 in the supplied research, despite the data snapshot providing o3 token prices. The research therefore contains a direct evidence mismatch between the benchmark dataset and the current official pricing page.

The practical break-even question is task-specific. Qwen3.5 is the natural first candidate for cost-sensitive batch workloads. o3 deserves a pilot when its speed or math behavior can reduce retries, shorten sessions, or improve completion quality enough to offset the higher listed token price. The available material does not measure those trade-offs.

Qwen3.5 397B A17B (Reasoning) should be the first test for value-focused general reasoning, while o3 should be tested for fast output and math-heavy workloads.

Choose Qwen3.5 397B A17B (Reasoning) when your application processes many tokens, can tolerate slower generation, and benefits from the supplied 33.7 intelligence score or 48.2 coding score. This path is especially reasonable for asynchronous code analysis, batch transformation, internal research tools, and agent tasks where generation speed is not the main user-facing constraint.

Choose o3 when interactive output speed is central, or when the supplied 88.3 math score matches the task you need to solve. The 128.056 median output rate makes o3 the more compelling candidate for conversational interfaces, rapid code suggestions, and workflows where users watch the response arrive. The decision should remain conditional because the supplied official OpenAI pages do not confirm o3's current listing or stable alias.

Use a two-model pilot when the workload mixes these priorities. Route latency-sensitive requests to o3 and cost-sensitive requests to Qwen3.5 only after confirming both endpoints, prices, quotas, and version behavior with the actual provider. The supplied research gives no verified Qwen3.5 deployment source, so Qwen3.5 should not enter production solely because its benchmark price is lower.

A sensible evaluation should measure task success, retry frequency, time to usable answer, total generated tokens, tool-call completion, and reviewer effort. The supplied sources do not report these production metrics. They also do not document context windows, output limits, multimodal support, stable aliases, or known failure modes for the two model names.

The final recommendation is therefore conditional: start with Qwen3.5 for economical reasoning and coding exploration, start with o3 for speed and math exploration, and make availability verification a release gate for either model.

o3 and Qwen3.5 397B A17B (Reasoning) both require deployment verification because the supplied research does not provide a complete, matching operational specification.

The OpenAI model directory identifies GPT-5.6 Sol, GPT-5.6 Terra, and GPT-5.6 Luna as current frontier models in the supplied research, but it does not list o3. The same research says that the official OpenAI pages do not clarify whether o3 remains directly callable, has a stable alias, or has been formally replaced.

Qwen3.5 has an even larger evidence gap. The supplied research finds no official release announcement, developer documentation, pricing page, stable API alias, current callable status, replacement relationship, or reliable community testing record for Qwen3.5 397B A17B (Reasoning). That means the model name may identify a benchmark entry without identifying a production-ready integration.

Neither model has a supplied context-window value. Neither has verified output limits or multimodal capability details. The research also contains no reliable Reddit, Hacker News, or X material describing coding experience, speed perception, behavior preferences, limitations, or failure scenarios.

The data attribution is Data provided by https://artificialanalysis.ai/. The snapshot is useful for directional comparison, but developers should validate the endpoint and repeat representative tasks before committing architecture, budgets, or user-facing promises.