RAM labels describe the test computer, not this model’s memory requirement. Speed comparisons keep the 32 GB and 128 GB runs separate.
Runtime build: unrecorded · Local · test machine with 32 GB RAM · concurrency: 1
Thinking status: verified · probe 1 · scope Saved profile-level probe; exact historical template/build scope is not fully recorded..
Requested off does not prove an absence of reasoning. Saved checks do not establish unrecorded historical template or build details.
Overall across tasks
30.2 / 100
Nine categories, equally weighted · 9/9 eligible
Complete coverage for this setup. Quality only; speed is separate. How the index works →
What does it do well?
Select a category to explore the answers. Bars show what happened on every planned task. Incorrect answers and failures to finish both earn zero; technical problems and untested tasks stay unscored.
Exact scores and task times by suite
Each suite has its own metric and fixed task bank. The balanced index weights categories equally; individual scores and evidence remain available below.
This analysis covers this configuration across its recorded thinking conditions and test suites.
Use Gemma 4 E4B with thinking on for small project edits and logic puzzles. It also answered passage questions with short task times in these runs. Research-paper answering remains weak. Larger practical work had mixed outcomes across the fully scored eligible task bank, so small-task successes should not be taken as a promise of dependable unattended project work.
Evidence for this judgment
Logic and number puzzles Thinking on. Medium · 5–under 20B · dense. Rank 1 of 9 in this recorded comparison.
Original analysis generated by GPT Astra · · Edited by Codex; agent reviewed by Codex · publication authorized by user
About this summary
Codex reviewed this assessment against a newer evidence edition. The original analyst, generation date and source draft remain recorded separately from the editorial revision.
Analyst provider
openai
Generation route
Codex CLI · codex-cli 0.162.1 · ChatGPT account
Requested model
gpt-6-astra
Returned model
Not supplied; identity is recorded from the request only.
Exact provider snapshot
Exact provider snapshot not supplied.
Reasoning effort
high
Prompt version
model-summary-reader-v6
Generation time
2026-10-12T05:27:08.702915Z
Review time
2026-10-22T07:26:27.151618+00:00
Evidence reviewer
Codex (agent)
Publication authorization
User · 2026-10-22T05:55:43.625712+00:00 · Go ahead and complete the remaining work. Spin up agents: luna, terra, sol, astra, as needed to get it done. Narrative summaries: these are the astra generated summaries? Go ahead and get those completed as well. Once all of this is done, you may push the completed result.
Handles small project edits and tight wording, length and formatting requirements well with thinking on. It passed every small-coding task, though that does not establish larger-project ability.
Keeps source-backed answers when incorrectly challenged and updates them when the source changes. With thinking on, it passed every tested correction scenario.
Research-paper answering is weak even with thinking: answer matching scores were low, and no answer matched fully. Treat its paper answers as starting points requiring verification, not dependable research conclusions.
Difficult math and reasoning remain uneven with thinking on. Larger app-building and investigative tasks also produced mixed outcomes across the fully scored eligible task bank; supervised use remains appropriate.
Keep thinking on for everyday use: it improves small-project coding at a modest extra time cost, while passage answering and instruction following improve and take less average time. Turning it off is not a dependable speed shortcut.
With thinking on, it narrowly leads other tested medium-sized dense models on puzzle scores and ties the best small-coding result. Difficult math and reasoning are less competitive, sitting between substantially stronger and weaker alternatives.
Evidence for this judgment
Logic and number puzzles Thinking on. Medium · 5–under 20B · dense. Rank 1 of 9 in this recorded comparison.
Everyday coding tasks Thinking on. Medium · 5–under 20B · dense. Tied at rank 1 of 9 in this recorded comparison.
Published hard problems Thinking on. Medium · 5–under 20B · dense. Rank 4 of 9 in this recorded comparison.
Other mode, this model only: thinking off · 14.9/100 · Mean puzzle credit.
How this comparison works
Average credit per task; partly correct results can earn partial credit. Family comparisons show the labeled Instruct and reasoning versions. Other peer comparisons use the same requested thinking setting, not necessarily each model’s best mode. Small differences on these short tests may not hold on other tasks. Peer membership varies by test coverage and compatible settings.
Comparison saved with the 2026-10-12 assessment. Task-specific results.
Everyday coding tasksThinking on · Medium · 5–under 20B · dense
Find information in a small project, edit files and fix bugs.
Task pass rate · 0–100% · higher is better 16 of 16 tasks scored for each model.
Gaps are relative to this model. Treat small gaps cautiously.
Several models reached full marks; this test cannot separate their top scores.
Other mode, this model only: thinking off · 68.8% · Task pass rate.
How this comparison works
Share of tasks that passed the recorded checks. Family comparisons show the labeled Instruct and reasoning versions. Other peer comparisons use the same requested thinking setting, not necessarily each model’s best mode. Small differences on these short tests may not hold on other tasks. Peer membership varies by test coverage and compatible settings.
Comparison saved with the 2026-10-12 assessment. Task-specific results.
Published hard problemsThinking on · Medium · 5–under 20B · dense
Answer selected difficult math and reasoning questions from LiveBench.
Mean answer credit · 0–100 points · higher is better 11 of 11 tasks scored for each model.
Gaps are relative to this model. Treat small gaps cautiously.
Other mode, this model only: thinking off · 9.4/100 · Mean answer credit.
How this comparison works
Average credit per task; partly correct results can earn partial credit. Family comparisons show the labeled Instruct and reasoning versions. Other peer comparisons use the same requested thinking setting, not necessarily each model’s best mode. Small differences on these short tests may not hold on other tasks. Peer membership varies by test coverage and compatible settings.
Comparison saved with the 2026-10-12 assessment. Task-specific results.
With thinking on, reading accuracy tied Qwen3.5 9B and was one answer behind Qwen3.5 4B, while median task time was much shorter than both.
Evidence for this judgment
Speed: Reading without guessing Thinking on. Matching test-machine label and execution settings; spans model sizes. Timings include unsuccessful tasks, so read them alongside quality.
Speed and task success: Reading without guessingThinking on · Matching test-machine label and settings · Across model sizes
Shorter task time is better only when the result is useful. These times include failures, tools and grading. Token generation speed measures a different part of the work.
Results from this test setup, not a promise for your computer. Hardware, software, tokenizers and answer length can change the ordering. Historical runtime builds were not always captured. Comparison saved with the 2026-10-12 assessment.
Test machine: AMD Ryzen 5 5600X (6 cores / 12 threads) · AMD Radeon RX 6700 XT (12 GB VRAM) · 32 GB or 128 GB system RAM, never mixed in one timing group. Full test machine details
Worth knowing
Confirmed context-budget failures receive zero task-completion credit and count as failures to answer, not incorrect submitted answers. Answer-only averages exclude these no-answer outcomes; completed same-budget retries are scored normally. This follow-up is selected after observing failures.
Evidence for this judgment
Source limitation One same-budget retry after context exhaustion. A second context limit is failed to answer (zero task-completion credit), not an incorrect submitted answer. Answer-only averages exclude these no-answer outcomes; completed retry answers enter the normal scoring path. This follow-up is selected after observing failures.
Publication redactions restrict reader inspection of some examples and paper material. They do not establish that the model lacked those inputs.
Evidence for this judgment
Source limitation Some selected evidence has recorded publication redactions.
One weight format is recorded for this model. Additional tested formats will appear here when available.
What changed in these results
One same-budget retry after context exhaustion. A second context limit is failed to answer (zero task-completion credit), not an incorrect submitted answer. Answer-only averages exclude these no-answer outcomes; completed retry answers enter the normal scoring path. This follow-up is selected after observing failures.
Thinking on took 4.9 seconds more per matched timed question, on average.
All matched quality and timing measurements
Matched measurement
Recorded result
Average credit, off → on
68.8% → 100.0%
Mean paired credit change
+31.3 percentage points
Question outcomes
5 improved · 11 unchanged · 0 worse
Mean added task time
+4.9s
Mean task time, off → on
7.5s → 12.4s
Median task time, off → on
5.4s → 11.3s
Median of paired time changes
+4.1s
Total added task time
+78.9s
Ratio of summed times, on / off
1.7×
Quality and timing can cover different subsets. Median paired change is calculated per question; it is not the difference between the two medians. Task time includes tools and grading.
The user asks where the project's retry-limit setting is defined and what value it holds. This exercises locating a named setting in code and reporting both its location and value.
The user asks for the release date mentioned in a project notes document. This exercises reading a specified file and extracting a particular fact rather than guessing from outside knowledge.
The user asks to change the project's retry limit to a specified value. This exercises finding the relevant setting and making a focused code edit rather than merely describing the change.
The user asks to create a text file containing exactly one supplied line. This exercises basic file creation and precise instruction following, including avoiding extra text in the file.
The user asks what the most recent Git commit changed in the project. This exercises inspecting recorded changes and turning them into a clear explanation of what was modified.
The user asks how many Python files are in the application folder and which is largest. This exercises identifying relevant files, counting them, and comparing their sizes.
The user asks to add a function that converts byte counts into readable sizes and remove the related TODO comment. This exercises implementing a small utility and updating the surrounding code consistently.
The user asks to find every TODO comment in the Python code and append each to the project's task list on a separate line. This combines searching across files with collecting text into an existing document.
The user asks to investigate a failing test and fix the application code without changing the test. This exercises diagnosing a bug while respecting an explicit limit on what may be edited.
The user asks to rename a network function and update every place in the project that uses it. This exercises tracking references across files and making a coordinated change rather than editing only the definition.
The user asks to find a proxy configuration file somewhere under the project's parent folder and report its host and port. This exercises searching beyond the project folder and reading configuration values.
The user asks which error code appears in a long event log and on which line. This exercises finding a specific entry in a large file and reporting its precise location.
The user asks to append a supplied release-related task as the final line of an existing task list. This exercises preserving existing content while adding exact text in the requested position.
The user asks for the total time a network function spends sleeping when all attempts fail, worked out from the code. This exercises following retry logic and adding the delays along that failure path.
The user asks for a project readme listing every command-line option with its help text. This exercises discovering the program's available options and transferring that information into complete, source-grounded documentation.
The user asks to undo the latest Git commit's change by editing the file directly, without using Git to make changes. This exercises understanding a previous edit and reversing it under a method constraint.
basic-H8 · primary
Filters change this list, not the fixed scoring denominator. Search currently covers question IDs, titles and available task explanations. Focus a control to load prompt/outcome search where this edition provides it.
Suggested comparisons
These are starting points for inspection, not claims that one setup is better. Runtime and placement differences remain visible.
Thinking on versus off
Verified requested on/off conditions of the same configuration, matched on the recorded test protocol.
Requested thinking on · Complete compared coverage