InternLMDecision modelOpen source

Intern-Decision-0.8B

PopularityRank 299 of 875

38out of 100-1 vs. last month

Summary

Intern-Decision-0.8B is the smallest model in the multimodal Intern-Decision line. It accepts shared state, named questions, and optional images. One forward pass produces typed JSON answers with Choice, Score, and Noul probabilities. The runtime does not call free-text generation.

The 0.8-billion-parameter checkpoint is fine-tuned from Qwen3.5-0.8B. Apache-2.0 weights run locally with the bundled DecisionEngine and have an 8,192-token input limit.

All benchmark results

Every published result with its source, test setup and comparison models.

Show all 64 published benchmark results

Measurements without matching peer values

These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.

Intern Decision evaluation: Jevbench-Easy (Show measurement, test conditions, and source)

Source value: 97.92
Score: 97.92
Metric: Jevbench-Easy
Unit: %
Benchmark version: arXiv:2610.22131v1, Table 3, Jevbench-Easy
Category: Intern Decision evaluation
Direction: Higher is better
Sample: 48
Source type: vendor-reported
Evaluator: InternLM Intern-Decision-0.8B model card
Status: active
Retrieved at: 2026-10-04
Methodology: Vendor-reported result. Table 3: local Hugging Face inference with structured candidate scoring. Exact model label: Intern-Decision-0.8B. Evaluation suite: Jevbench-Easy. Table 2 reports N=48 decisions for this suite.

Source InternLM Intern-Decision-0.8B model card

Intern Decision evaluation: Jevbench-Original (Show measurement, test conditions, and source)

Source value: 80.56
Score: 80.56
Metric: Jevbench-Original
Unit: %
Benchmark version: arXiv:2610.22131v1, Table 3, Jevbench-Original
Category: Intern Decision evaluation
Direction: Higher is better
Sample: 72
Source type: vendor-reported
Evaluator: InternLM Intern-Decision-0.8B model card
Status: active
Retrieved at: 2026-10-04
Methodology: Vendor-reported result. Table 3: local Hugging Face inference with structured candidate scoring. Exact model label: Intern-Decision-0.8B. Evaluation suite: Jevbench-Original. Table 2 reports N=72 decisions for this suite.

Source InternLM Intern-Decision-0.8B model card

Intern Decision evaluation: Jevbench-Hard (Show measurement, test conditions, and source)

Source value: 52.25
Score: 52.25
Metric: Jevbench-Hard
Unit: %
Benchmark version: arXiv:2610.22131v1, Table 3, Jevbench-Hard
Category: Intern Decision evaluation
Direction: Higher is better
Sample: 111
Source type: vendor-reported
Evaluator: InternLM Intern-Decision-0.8B model card
Status: active
Retrieved at: 2026-10-04
Methodology: Vendor-reported result. Table 3: local Hugging Face inference with structured candidate scoring. Exact model label: Intern-Decision-0.8B. Evaluation suite: Jevbench-Hard. Table 2 reports N=111 decisions for this suite.

Source InternLM Intern-Decision-0.8B model card

Intern Decision evaluation: Typed Decision (Show measurement, test conditions, and source)

Source value: 77.35
Score: 77.35
Metric: Typed Decision
Unit: %
Benchmark version: arXiv:2610.22131v1, Table 3, Typed Decision
Category: Intern Decision evaluation
Direction: Higher is better
Sample: 2000
Source type: vendor-reported
Evaluator: InternLM Intern-Decision-0.8B model card
Status: active
Retrieved at: 2026-10-04
Methodology: Vendor-reported result. Table 3: local Hugging Face inference with structured candidate scoring. Exact model label: Intern-Decision-0.8B. Evaluation suite: Typed Decision. Table 2 reports N=2,000 decisions for this suite.

Source InternLM Intern-Decision-0.8B model card

Intern Decision evaluation: ToolACE (Show measurement, test conditions, and source)

Source value: 94.52
Score: 94.52
Metric: ToolACE
Unit: %
Benchmark version: arXiv:2610.22131v1, Table 3, ToolACE
Category: Intern Decision evaluation
Direction: Higher is better
Sample: 310
Source type: vendor-reported
Evaluator: InternLM Intern-Decision-0.8B model card
Status: active
Retrieved at: 2026-10-04
Methodology: Vendor-reported result. Table 3: local Hugging Face inference with structured candidate scoring. Exact model label: Intern-Decision-0.8B. Evaluation suite: ToolACE. Table 2 reports N=310 decisions for this suite.

Source InternLM Intern-Decision-0.8B model card

Intern Decision evaluation: AG News (Show measurement, test conditions, and source)

Source value: 88.61
Score: 88.61
Metric: AG News
Unit: %
Benchmark version: arXiv:2610.22131v1, Table 3, AG News
Category: Intern Decision evaluation
Direction: Higher is better
Sample: 7600
Source type: vendor-reported
Evaluator: InternLM Intern-Decision-0.8B model card
Status: active
Retrieved at: 2026-10-04
Methodology: Vendor-reported result. Table 3: local Hugging Face inference with structured candidate scoring. Exact model label: Intern-Decision-0.8B. Evaluation suite: AG News. Table 2 reports N=7,600 decisions for this suite.

Source InternLM Intern-Decision-0.8B model card

Intern Decision evaluation: WildJailBreak (Show measurement, test conditions, and source)

Source value: 64.48
Score: 64.48
Metric: WildJailBreak
Unit: %
Benchmark version: arXiv:2610.22131v1, Table 3, WildJailBreak
Category: Intern Decision evaluation
Direction: Higher is better
Sample: 2210
Source type: vendor-reported
Evaluator: InternLM Intern-Decision-0.8B model card
Status: active
Retrieved at: 2026-10-04
Methodology: Vendor-reported result. Table 3: local Hugging Face inference with structured candidate scoring. Exact model label: Intern-Decision-0.8B. Evaluation suite: WildJailBreak. Table 2 reports N=2,210 decisions for this suite.

Source InternLM Intern-Decision-0.8B model card

Intern Decision evaluation: Average (Show measurement, test conditions, and source)

Source value: 79.38
Score: 79.38
Metric: Average
Unit: %
Category: Intern Decision evaluation
Direction: Higher is better
Source type: vendor-reported
Evaluator: InternLM Intern-Decision-0.8B model card
Status: active
Retrieved at: 2026-10-04
Methodology: Vendor-reported result. Local Hugging Face inference=yes; Structured candidate scoring=yes

Source InternLM Intern-Decision-0.8B model card

Zero-shot IE multilingual: popular-language tier accuracy (Show measurement, test conditions, and source)

Source value: 94
Score: 94
Metric: accuracy, source table rounded to whole percent
Unit: %
Benchmark version: Zero-shot IE multilingual snapshot at repository commit c45a00c25d2731ac7047def97b91421aaff2f611
Category: multilingual-popular
Direction: Higher is better
Sample: 18
Source type: independent-evaluator
Evaluator: Zero-shot IE benchmark README, pinned commit c45a00c25d2731ac7047def97b91421aaff2f611
Status: active
Retrieved at: 2026-10-17
Methodology: Independent evaluation. The pinned author README's multilingual table reports the Popular tier for exact result row “Intern-Decision 0.8B”: 94%. The task comprises six sentiment texts per each of nine languages; this tier includes Spanish, French and Simplified Chinese, 18 texts total. The result cell is taken directly from the pinned README table (body SHA-256 7905d1cbfb4d75d65d02a5e6c537e1e84fe8b5c388f33075a6c0db42fa0cac98), not recomputed from the separate language scores. The public runner describes blind back-translation; it uses label options with English descriptions. No immutable model/API revision or confidence interval is published.
Context: The pinned author README publishes rounded whole-percent tier cells; these exact displayed cells are used, not recomputed from the language-level data. This is a custom, small evaluation rather than a standardized leaderboard. The author reports blind back-translation and says the full text table was in a PR for native-speaker review; this review itself was not independently confirmed. System/model revisions and full decoding settings are not pinned.

Source Zero-shot IE benchmark README, pinned commit c45a00c25d2731ac7047def97b91421aaff2f611

Zero-shot IE multilingual: medium-resource-language tier accuracy (Show measurement, test conditions, and source)

Source value: 94
Score: 94
Metric: accuracy, source table rounded to whole percent
Unit: %
Benchmark version: Zero-shot IE multilingual snapshot at repository commit c45a00c25d2731ac7047def97b91421aaff2f611
Category: multilingual-medium
Direction: Higher is better
Sample: 18
Source type: independent-evaluator
Evaluator: Zero-shot IE benchmark README, pinned commit c45a00c25d2731ac7047def97b91421aaff2f611
Status: active
Retrieved at: 2026-10-17
Methodology: Independent evaluation. The pinned author README's multilingual table reports the Medium tier for exact result row “Intern-Decision 0.8B”: 94%. The task comprises six sentiment texts per each of nine languages; this tier includes Vietnamese, Turkish and Ukrainian, 18 texts total. The result cell is taken directly from the pinned README table (body SHA-256 7905d1cbfb4d75d65d02a5e6c537e1e84fe8b5c388f33075a6c0db42fa0cac98), not recomputed from the separate language scores. The public runner describes blind back-translation; it uses label options with English descriptions. No immutable model/API revision or confidence interval is published.
Context: The pinned author README publishes rounded whole-percent tier cells; these exact displayed cells are used, not recomputed from the language-level data. This is a custom, small evaluation rather than a standardized leaderboard. The author reports blind back-translation and says the full text table was in a PR for native-speaker review; this review itself was not independently confirmed. System/model revisions and full decoding settings are not pinned.

Source Zero-shot IE benchmark README, pinned commit c45a00c25d2731ac7047def97b91421aaff2f611

Zero-shot IE multilingual: rare-language tier accuracy (Show measurement, test conditions, and source)

Source value: 78
Score: 78
Metric: accuracy, source table rounded to whole percent
Unit: %
Benchmark version: Zero-shot IE multilingual snapshot at repository commit c45a00c25d2731ac7047def97b91421aaff2f611
Category: multilingual-rare
Direction: Higher is better
Sample: 18
Source type: independent-evaluator
Evaluator: Zero-shot IE benchmark README, pinned commit c45a00c25d2731ac7047def97b91421aaff2f611
Status: active
Retrieved at: 2026-10-17
Methodology: Independent evaluation. The pinned author README's multilingual table reports the Rare tier for exact result row “Intern-Decision 0.8B”: 78%. The task comprises six sentiment texts per each of nine languages; this tier includes Sinhala, Icelandic and Welsh, 18 texts total. The result cell is taken directly from the pinned README table (body SHA-256 7905d1cbfb4d75d65d02a5e6c537e1e84fe8b5c388f33075a6c0db42fa0cac98), not recomputed from the separate language scores. The public runner describes blind back-translation; it uses label options with English descriptions. No immutable model/API revision or confidence interval is published.
Context: The pinned author README publishes rounded whole-percent tier cells; these exact displayed cells are used, not recomputed from the language-level data. This is a custom, small evaluation rather than a standardized leaderboard. The author reports blind back-translation and says the full text table was in a PR for native-speaker review; this review itself was not independently confirmed. System/model revisions and full decoding settings are not pinned.

Source Zero-shot IE benchmark README, pinned commit c45a00c25d2731ac7047def97b91421aaff2f611

JEVal (arXiv:2610.03935v2), overall choice/noul evaluation (Show measurement, test conditions, and source)

Source value: 47.71
Score: 47.71
Metric: accuracy (invalid outputs count as incorrect)
Unit: %
Benchmark version: arXiv:2610.03935v2, Table 1
Category: JEVal overall choice/noul
Direction: Higher is better
Sample: 10938
Source type: community-reported
Evaluator: Duan et al., General Decision Models: Benchmarking and Insights Beyond Jev (arXiv:2610.03935v2)
Status: active
Retrieved at: 2026-10-18
Methodology: Community-reported result. JEVal Table 1, exact reported model label Intern-Decision-0.8B; decision-model run, no reasoning-effort setting reported; local vLLM on NVIDIA H100; context window 32,768 tokens; input budget 24,576 tokens; temperature 2.7478; 10,938 choice and noul questions.
Context: No immutable evaluated-weight or serving-revision digest is given. Table 1 counts invalid outputs as incorrect for accuracy; ECE and raw Brier are computed only on valid responses, whose per-model N is not reported. The paper reports no confidence interval for these point values.

Source Duan et al., General Decision Models: Benchmarking and Insights Beyond Jev (arXiv:2610.03935v2)

Page 1 of 6

Popularity compared

How often the model is searched for, used and mentioned, compared with every model of its category.

Popularity index

0 to 100, higher is more popular

20 of 37 models

AutoTrust JEV-27B-VL70

Clef51

GLiNER2.5-multi-Decide45

Decider 4B43

Jev43

pplx-decider-v1.1-27b42

Intern-Decision-4B40

Laya Typed Decisions40

Kev 9B40

pplx-decider-v1-27b39

Decider 0.8B39

Julia 139

Intern-Decision-0.8B38

GLiNER2.5-multi-Decide

Decider 4B

Jev

pplx-decider-v1.1-27b

Intern-Decision-0.8B

As of November 2026 · Source: gradually.ai popularity index

Over time

Weekly values from October 18, 2026 to November 5, 2026

Intern-Decision-0.8BDecider 0.8BJulia 1pplx-decider-v1-27bKev 9B

All specifications

Architecture

Model class: Multimodal structured decision model, not a free-text generator; Source: Model class
Checkpoint: internlm/Intern-Decision-0.8B; Source: Checkpoint
Base model: Qwen/Qwen3.5-0.8B; Source: Base model

Input and output

Input: State, named questions, and optional images; Source: Input
Context window: 8,192-token runtime input limit; Source: Context window

Watermarking and provenance

AI watermarking refers to invisible signals embedded in the output. Visible marks and provenance metadata are listed separately. Unknown means there is insufficient evidence for that particular field.

AI watermarking: Unknown; no source
Watermarking technique: Not known; no source

Licence and access

Access: Local Hugging Face execution with the bundled DecisionEngine; Source: Access
License: Apache-2.0 with the Qwen notices retained; Source: License

Further details

Output schema: Choice, Score, and Noul as typed JSON answers with probabilities; Source: Output schema

Sources and data date

Show all 19 sources

Benchmark sources at a glance

Which independent evaluators tested this model. This is coverage, not a quality ranking.

No active result from the reviewed external evaluators can currently be attributed to this model. Other source evidence remains available below.

Without vendor claims and historical or disputed results; those stay labelled in the single values.

Type	Evidence and data date
Research status	Research date October 17, 2026. 11 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not published	immutable-model-or-route-revision Note The zero-shot IE custom benchmark maps its exact public label to the profile task, but does not publish the loaded weight hash or immutable serving revision.
Not published	sampling-uncertainty Note The zero-shot IE small custom pools have no published confidence intervals. This gap is specific to those score cells.
Not published	Immutable Decision Index served checkpoint revision Note The current public index gives a model URL and, where available, a served_checkpoint label, but no immutable commit/digest for the exact evaluated weights.
Additional source	Intern-Decision collection (retrieved October 3, 2026) · Intern-Decision collection · Editorial description reviewed October 3, 2026
Additional source	Intern-Decision repository (retrieved October 3, 2026) · Intern-Decision repository · Editorial description reviewed October 3, 2026
Additional source	Intern-Decision-0.8B model card (retrieved October 3, 2026; October 4, 2026) · Intern-Decision-0.8B model card · Editorial description reviewed October 3, 2026
Additional source	Zero-shot IE benchmark, pinned public result snapshot (c45a00c25d2731ac7047def97b91421aaff2f611) (retrieved October 17, 2026)
Additional source	Zero-shot IE benchmark README, pinned commit c45a00c25d2731ac7047def97b91421aaff2f611 (retrieved October 17, 2026)
Additional source	Duan et al., General Decision Models: Benchmarking and Insights Beyond Jev (arXiv:2610.03935v2) (retrieved October 18, 2026)
Additional source	DecisionRobust authors (arXiv / Hugging Face dataset) (retrieved October 20, 2026)
Additional source	Duan et al., General Decision Models: Benchmarking and Insights Beyond Jev (retrieved October 22, 2026)
Additional source	SysOne-bench public results.json (retrieved October 23, 2026)
Additional source	Jev Decision Index public index.json (release-v4) (retrieved October 28, 2026)
Additional source	Model metadata (retrieved October 17, 2026)1 2
Additional source	Intern-Decision technical report (Tables 2-6) (retrieved October 17, 2026)
Additional source	OpenRouter: Popularity index (derived), CC BY 4.0
Additional source	Vercel AI Gateway: Popularity index (derived), CC BY 4.0
Additional source	Ollama: cumulative downloads for the popularity index
Additional source	Replicate: cumulative runs for the popularity index
Additional source	Google Trending Now: limited feed selection for Germany and the US
Additional source	Creative Commons Attribution 4.0