ByteDance Official Model Card: Seed2.1 Turbo Multitask Benchmarks vs. Pro

One-sentence takeaway

The official tables show Turbo maintaining an overall level close to Pro on office, coding, vision, and video tasks, but with clear gaps on Agent Startup, visual perception, and some video motion-understanding tasks. Routing should therefore be based on task families rather than applying a uniform downgrade.

Use cases

• Suitable tasks: Use the official scores to establish task-family priors for Turbo/Pro, then design local retesting and routing rules with the same harness.

• Unsuitable tasks: Treating the official scores as the success rate for your own repository, or overlooking differences in tools, prompts, model snapshots, and evaluation implementations.

• Applicable model versions: Seed2.1 Pro and Seed2.1 Turbo; the table also lists Claude Opus 4.7, GPT-5.5, Gemini 3.1 Pro, and other comparisons.

• Applicable clients, agents, or APIs: The official Seed page and the Doubao/Volcengine Ark ecosystem; the page does not disclose API request details for each benchmark.

• Recommended reasoning tier and parameters: Not publicly disclosed; the page provides scores but does not uniformly disclose temperature, maximum output, tool versions, or the number of samples.

Test environment

• Evaluator: ByteDance Seed official team.

• Capabilities covered: Knowledge, reasoning, high-economic-value office work, long-chain end-to-end coding, terminal use, debugging, multimodal reasoning, vision, spatial reasoning, long context, long-video understanding, and motion understanding.

• Comparisons: Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro in the official table, plus Gemini 3.5 Flash for the video section.

• Input/configuration: The full prompt, data version, tools, number of samples, confidence intervals, and harness are not publicly disclosed.

Input/configuration

The page presents the results in an interactive model-card table; entries marked “w. Tool” explicitly identify a tool version, but provide neither the tool schema nor request examples. The Turbo cell for ProgramBench is displayed verbatim as 0/0/49.4; the page does not explain the meaning of the three sub-values in the body text.

Result data

The following figures are percentages/scores shown directly in the official table, transcribed in the page’s Pro/Turbo column order:

The page also shows Turbo at 88.0 on BeyondAIME (Pro 87.0), 82.5 on CharXiv-RQ (with tools) (83.6), and 11.0 on ZEROBench (with tools) (20.0). The parenthesized values are additional values in the same page cell; the official source does not explain their statistical meaning in readable body text.

Conclusion

Turbo is not simply a scaled-down version of Pro: it is close to or higher than Pro on benchmarks such as BeyondAIME and Workspace Bench, but trails more clearly on Agent Startup Bench, BabyVision, TOMATO, and some coding/debugging tasks. It is better suited as the default route when cost/throughput is the priority, with a Pro fallback retained for high-risk, long-chain tasks.

Limitations

• All scores come from the official model page and were not independently rerun; inputs, prompts, number of samples, tool versions, and confidence intervals are unavailable.

• The page shows model snapshots and table results, while the current API endpoints may change; the date and snapshot should be recorded again when retesting.

• The statistical definitions of slash-separated and parenthesized values are not public, so they should not be decomposed or averaged independently.

• Score differences cannot be directly converted into real-world repository success rates, cost, or latency.

Reproduction steps

1. Record the current Turbo/Pro API model snapshots, thinking mode, tool set, context, and pricing.

2. Build a real task set bucketed into office, coding, terminal, vision, and video tasks, and ensure both models use the same harness.

3. Record first-pass completion, correction rounds, recovery from tool failures, independent tests, tokens, latency, and per-task cost.

4. Set the default Turbo route and Pro fallback based on task-family results, and regularly compare them against updates to the official tables.

Source excerpts or observations (for compliant short quotations only)

• The official page displays Turbo and Pro side by side rather than providing only an aggregate family score.

• The results table treats w. Tool, long video, and multimodal long context as separate entries, indicating that tools and input modalities must be retained as evaluation dimensions.