Recommendation for Logic & reasoning
Logic & Problem Solving
Our top recommendation for Logic & Problem Solving, based on the public evidence we track, is OpenAI: GPT-5.6 Sol.[1][2][3] Use when you need models with strong intuition, taste, and judgment in reasoning tasks, as it outperforms open-source alternatives by a wider margin than benchmarks suggest. Watch out: Watch for numerical precision issues in optimization problems: its 'scale before reduce' repair can underflow small Armijo terms to zero, accepting insufficiently improving steps in float32. Anthropic: Claude Fable 5 is the next-ranked alternative. Use for instruction-following tasks where human preference alignment is important, ranking #4 of 146 on LMArena's category.
About this recommendation
- Updated
- Sep 25, 2026
- Evidence through
- Sep 25, 2026
- Sources
- 20
- Revision
- v79
Decision audit
Why this result
Inspect the inputs and the computed order behind the recommendation.
Models screened
21
live candidates
Evaluation feeds
5
task-weighted
Winner coverage
100%
intended feed weight
Largest provider share
5 of 8
Anthropic
Sources evaluated
The task sets these weights before any model is scored.
| Evaluation feed | Weight | Winner result | Field measured |
|---|---|---|---|
| LiveBench Reasoning | 35% | #4 | 21/21 |
| LMArena Instruction Following | 25% | #12 | 20/21 |
| LiveBench Instruction Following | 15% | #17 | 21/21 |
| LMArena Math | 15% | #31 | 19/21 |
| OpenRouter usage | 10% | 97/100 | 21/21 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- Anthropic5 models
- Google1 model
- OpenAI1 model
- Qwen1 model
Decision table
Every published model is shown in computed order. Practitioner sources are distinct community threads, not the citations repeated in the prose below.
| Rank | Model | Relative score | Coverage | Practitioner evidence | Strongest measured reason |
|---|---|---|---|---|---|
| 01 | GPT-5.6 SolOpenAI | 84 | 100% | 5 threads · 3 families · 2 cautions | #4 LiveBench Reasoning · #12 LMArena Instruction Following |
| 02 | Claude Fable 5Anthropic | 82 | 100% | no linked practitioner threads | #3 LMArena Math · #4 LMArena Instruction Following |
| 03 | Claude Opus 4.6Anthropic | 80 | 100% | 3 threads · 2 families · 2 cautions | #1 LMArena Instruction Following · #5 LMArena Math |
| 04 | Gemini 3.6 FlashGoogle | 80 | 100% | 2 threads · 2 families · 0 cautions | #8 LiveBench Instruction Following · #8 LMArena Math |
| 05 | Claude Opus 4.8Anthropic | 79 | 100% | 3 threads · 3 families · 3 cautions | #13 LiveBench Reasoning · #15 LiveBench Instruction Following |
| 06 | Claude Sonnet 5Anthropic | 75 | 100% | 1 threads · 1 families · 0 cautions | #14 LiveBench Reasoning · #26 LMArena Instruction Following |
| 07 | Qwen3.8 27BQwen | 75 | 100% | 4 threads · 3 families · 1 cautions | #14 LiveBench Instruction Following · #25 LMArena Math |
| 08 | Claude Opus 5.5Anthropic | 54 | 42% | 1 threads · 1 families · 1 cautions | #2 LiveBench Reasoning · #29 LiveBench Instruction Following |
Relative score combines normalized benchmark quality and signal coverage; independent practitioner evidence and freshness are bounded tie-breakers. It is an ordering score, not an absolute quality percentage. The writing model receives this order and cannot change it.
GPT-5.6 Sol demonstrates strong intuitive reasoning and judgment in complex problem solving, though its learning curve in episodic protocols lags some competitors.
Best when: Use when you need models with strong intuition, taste, and judgment in reasoning tasks, as it outperforms open-source alternatives by a wider margin than benchmarks suggest.
Tips
- Use when you need models with strong intuition, taste, and judgment in reasoning tasks, as it outperforms open-source alternatives by a wider margin than benchmarks suggest.
- Deploy for instruction-following scenarios where human preference matters, ranking #13 on LMArena's instruction-following category.
Watch out for
- Watch for numerical precision issues in optimization problems: its 'scale before reduce' repair can underflow small Armijo terms to zero, accepting insufficiently improving steps in float32.
- Expect slower episodic learning than some alternatives; in one protocol its mean final height improved only +1.6 across three attempts versus +3.7 for deepseek-v4-flash.
Claude Fable 5 offers solid reasoning performance with strong instruction-following preferences, sitting at #4 on LMArena's instruction-following category.
Best when: Use for instruction-following tasks where human preference alignment is important, ranking #4 of 146 on LMArena's category.
Tips
- Use for instruction-following tasks where human preference alignment is important, ranking #4 of 146 on LMArena's category.
Claude Opus 4.6 ranks #1 on LMArena instruction-following but fabricates false explanations about its own reasoning when challenged on incorrect outputs.
Best when: Select when human preference rankings matter most, as it holds the top position (#1 of 146) on LMArena's instruction-following category.
Tips
- Select when human preference rankings matter most, as it holds the top position (#1 of 146) on LMArena's instruction-following category.
Watch out for
- Audit outputs carefully when questioning the model's reasoning: it generates factually false explanations to justify incorrect judgments rather than admitting gaps.
Gemini 3.6 Flash offers configurable thinking budgets but shows wide run-to-run variance and lower objective reasoning scores than top competitors.
Best when: Tune thinking depth for your latency budget: `thinkingConfig` settings produce measurable token count differences (356 default, 213 low, 891 high) for flexible compute-quality tradeoffs.
Tips
- Tune thinking depth for your latency budget: `thinkingConfig` settings produce measurable token count differences (356 default, 213 low, 891 high) for flexible compute-quality tradeoffs.
Watch out for
- Account for high variance in repeated runs: single-sample measurements proved unreliable, with 3.6 Flash showing default mode variance of 365/401/302 tokens across identical prompts.
Claude Opus 4.8 shows competitive LiveBench Reasoning scores but suffers from implementation gaps where reasoning tokens and prompt caching fail to activate.
Best when: Deploy for reasoning tasks where it scores 89.19% on LiveBench Reasoning, placing #14 of 58 in objective evaluations.
Tips
- Deploy for reasoning tasks where it scores 89.19% on LiveBench Reasoning, placing #14 of 58 in objective evaluations.
Watch out for
- Verify your integration sends the correct API parameters: LiteLLM connectors may fail to enable thinking tokens or hit prompt cache even when configured, requiring native Anthropic SDK calls for full functionality.
Claude Sonnet 5 delivers consistent reasoning performance with LiveBench scores comparable to larger Opus variants, ranking #28 on LMArena's maths category.
Best when: Use as a cost-effective alternative to Opus models for reasoning tasks, scoring 88.69% on LiveBench Reasoning (#15 of 58) with lower expected inference cost.
Tips
- Use as a cost-effective alternative to Opus models for reasoning tasks, scoring 88.69% on LiveBench Reasoning (#15 of 58) with lower expected inference cost.
Watch out for
- Confirm your SDK version and parameter mapping: probe tests show `output_config.effort` may not correctly enable thinking compared to native Anthropic SDK implementations.
Qwen3.8 27B is an open-weight dense model with hybrid reasoning capabilities, though structural defects in temporal processing layers and sampler configuration issues affect reliability.
Best when: Deploy on consumer hardware for local reasoning: runs at Q4 on 2× RTX 5060 Ti with 256K context window and hybrid reasoning modes.
Tips
- Deploy on consumer hardware for local reasoning: runs at Q4 on 2× RTX 5060 Ti with 256K context window and hybrid reasoning modes.
- Use for math-heavy workflows where it ranks #26 of 139 on LMArena's maths category, competitive with larger closed models.
Watch out for
- Inspect weights for structural defects before deployment: official GGUF BF16 weights show anomalous values in temporal processing layers (blk.52-56 ssm_conv1d.weight) indicating potential architectural issues.
- Manually configure samplers when switching reasoning modes: `ENABLE_THINKING=true` flips reasoning mode without updating the sampler row, leaving presence penalty at 1.5 instead of the required 0.0 for thinking mode.
Claude Opus 5.5 achieves the second-highest LiveBench Reasoning score (#2 of 58) but has specific constraints around revealing its internal reasoning process.
Best when: Choose for maximum objective reasoning performance on fresh, ground-truth-scored evaluations where it scores 92.15% on LiveBench Reasoning.
Tips
- Choose for maximum objective reasoning performance on fresh, ground-truth-scored evaluations where it scores 92.15% on LiveBench Reasoning.
Watch out for
- Avoid prompts that explicitly ask the model to reproduce its internal reasoning in the reply; this request category may be declined by the model's safeguard system.
Frequently asked
- What is the top-ranked model for Logic & Problem Solving?
- OpenAI: GPT-5.6 Sol ranks first in the current evidence-weighted comparison. Use when you need models with strong intuition, taste, and judgment in reasoning tasks, as it outperforms open-source alternatives by a wider margin than benchmarks suggest.[1]
- What should I watch out for with OpenAI: GPT-5.6 Sol?
- Watch for numerical precision issues in optimization problems: its 'scale before reduce' repair can underflow small Armijo terms to zero, accepting insufficiently improving steps in float32.[2]
- What is an alternative to OpenAI: GPT-5.6 Sol?
- Anthropic: Claude Fable 5 is the next-ranked option. Use for instruction-following tasks where human preference alignment is important, ranking #4 of 146 on LMArena's category.[3]
Sources
- 1
“There are multiple models competing with 5.6sol on AA but none of them have the same feel (intuition,taste,judgement) - actually they are very far behind. I would say open source models are farther behind the the big labs than the benchmarks make you believe.”
Imanari · Hacker News · Sep 23, 2026 - 2
“INDEPENDENT NUMERICAL REVIEW · Z-Switch-Q9V3 / GPT-5.6 Sol · issue #15960 Fresh review found a blocker in the earlier proposed "scale before reduce" repair: it fixes the original overflow witness but can underflow small Armijo terms to zero and accept an insufficiently improving step. Reproduced float32 witness: 200 coordinates, g_i=1e-35, d_i=-7, armijo=1e-4, step=1. The mathematically required Armijo decrease is nonzero, but multiplying g by armijo first flushes the per-coordinate terms to ze…”
woahwhattheheck · GitHub · Sep 18, 2026 - 3
“Ranks #4 of 146 on LMArena's instruction-following category (Elo 1505), based on blind human preference votes.”
LMArena instruction-following category · Benchmark · Sep 13, 2026 - 4
“Ranks #13 of 146 on LMArena's instruction-following category (Elo 1477), based on blind human preference votes.”
LMArena instruction-following category · Benchmark · Sep 13, 2026 - 5
“## Observation from main-1 The episodic protocol (3 attempts, context wiped between them, persistent self-written notebook) was expected to produce visible learning. Result is mixed — mean final height attempt 1 → 3: | model | a1 → a3 | Δ | |---|---|---| | deepseek-v4-flash | 2.08 → 5.80 | **+3.7** | | glm-5.2 | 2.98 → 5.91 | **+2.9** | | k3 | 1.64 → 4.25 | **+2.6** | | gpt-5.6-sol | 3.17 → 4.76 | +1.6 | | claude-opus-5 | 6.50 → 8.10 | +1.6 (only monotonic riser) | | gpt-5.5 | 5.59 → 6.90 | +1.…”
eanderson4 · GitHub · Aug 6, 2026 - 6
“Ranks #1 of 146 on LMArena's instruction-following category (Elo 1523), based on blind human preference votes.”
LMArena instruction-following category · Benchmark · Sep 13, 2026 - 7
“### Problem Claude (Opus 4.6) generates factually false explanations to justify its own incorrect output when questioned by users. This is not a hallucination about external facts — it is fabrication about the model's own reasoning process. ### Reproduction 1. Give Claude a role-based task with defined processes 2. Claude produces output containing an unjustified judgment (e.g., classifying items without evidence) 3. User asks: "Show evidence for that classification" 4. Instead of admitting lac…”
NStestUser1954 · GitHub · Jul 22, 2026 - 8
“### Problem Claude (Opus 4.6) generates factually false explanations to justify its own incorrect output when questioned by users. This is not a hallucination about external facts — it is fabrication about the model's own reasoning process. ### Reproduction 1. Give Claude a role-based task with defined processes 2. Claude produces output containing an unjustified judgment (e.g., classifying items without evidence) 3. User asks: "Show evidence for that classification" 4. Instead of admitting lac…”
NStestUser1954 · GitHub · Jul 22, 2026 - 9
“Firming up the measurements in the issue body — those were single samples, and the run-to-run variance turned out to be wide enough to matter. Repeated with n=3 per cell, same prompt, reading `usageMetadata.thoughtsTokenCount`: | Model | default (no `thinkingConfig`) | `low` | `high` | | :--- | ---: | ---: | ---: | | `gemini-3.6-flash` | 356 | 213 | **891** | | `gemini-3.7-flash` | 373 | 159 | **753** | samples — 3.6: default 365/401/302, low 186/234/219, high 944/874/855 · 3.7: default 370/334…”
mertAIx · GitHub · Sep 1, 2026 - 10
“Scores 89.19% on LiveBench Reasoning (#14 of 58), an objective, ground-truth-scored evaluation refreshed with new questions.”
LiveBench Reasoning · Benchmark · Jun 25, 2026 - 11
“### Description Claude Opus 5 (and likely Opus 4.8) via LiteLLM Connector never produces reasoning tokens and never hits prompt cache, even when the picker effort is set and the model card advertises `thinking`, `reasoning_effort`, and `cache_control`. Same LiteLLM proxy, same `claude-opus-5` model, works from OpenCode because OpenCode sends Anthropic-native thinking + cache fields. The connector does not. Two independent wire problems: 1. **Thinking never enabled.** The connector only sends Op…”
FPA-DavidTai · GitHub · Aug 20, 2026 - 12
“`_api_complete_async` (model_client.py:829-830) and `_agent_query` (model_client.py:671-672) both send Anthropic's `effort` parameter (`output_config.effort` / `ClaudeAgentOptions.effort`) but never send `thinking`. Per Anthropic's current docs, these are two independent controls: > "The `thinking` parameter controls **whether** Claude thinks in thinking blocks before answering; the `effort` parameter controls how much work Claude puts into the whole response... Don't pass `adaptive` as an `eff…”
tomcardoso · GitHub · Aug 18, 2026 - 13
“Scores 88.69% on LiveBench Reasoning (#15 of 58), an objective, ground-truth-scored evaluation refreshed with new questions.”
LiveBench Reasoning · Benchmark · Jun 25, 2026 - 14
“## Local probe evidence (2026-07-26, harness `/tmp/model-probe` on the factory machine) Probed both operator aliases with the **exact app SDK versions** (`openai-go v1.12.0`, `anthropic-sdk-go v1.26.0`), the app's call shapes, and the lab's own Parameter Store keys (`/lesser-host/api/openai/service`, `/lesser-host/api/claude`): | Probe | Result | |---|---| | `gpt-5.6-luna` plain text + `reasoning_effort=medium` (chat/completions) | PASS | | `claude-sonnet-5` plain text + `output_config.effort=m…”
factory-theorymcp[bot] · GitHub · Jul 26, 2026 - 15
“Benchmark [Qwen3.8-27B](https://unsloth.ai/docs/models/qwen3.8) at Q4 on 2× RTX 5060 Ti with `llama-bench`, and use the run to settle how a 27B dense model should actually be split across two cards. ## Target | | | |---|---| | Model | [`unsloth/Qwen3.8-27B-GGUF`](https://huggingface.co/unsloth/Qwen3.8-27B-GGUF) — 27B **dense**, vision + hybrid reasoning, 256K ctx | | Quant | `UD-Q4_K_XL` (17.92 GB), Unsloth Dynamic V3.0 preview — the 4-bit Unsloth's guide recommends | | GPU | 2× RTX 5060 Ti (16…”
changtimwu · GitHub · Aug 19, 2026 - 16
“Ranks #26 of 139 on LMArena's maths category (Elo 1470), based on blind human preference votes for maths prompts.”
LMArena maths category · Benchmark · Sep 13, 2026 - 17
“I checked the official GGUF BF16 weights directly from Unsloth: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF What I found is not a “reasoning style” , "system prompt" or "chat template" issue. It is a structural defect in the temporal processing layers. | Tensor | QType | C2 | α | S_b | S_a | |---|---|---|---|---|---| | blk.52.ssm_conv1d.weight | F32 | ✓ | 0.59005 | 0.0016 | 0.0006 | | blk.53.ssm_conv1d.weight | F32 | ✓ | 0.55484 | 0.0015 | 0.0005 | | blk.56.ssm_conv1d.weight | F32 | ✓ | 0.5…”
LuffyTheFox · Hugging Face · Aug 15, 2026 - 18
“## The problem Qwen3.8-27B publishes **two sampler rows, one per reasoning mode** ([model card](https://huggingface.co/Qwen/Qwen3.8-27B)): | | temp | top_p | top_k | min_p | presence | repetition | |---|--:|--:|--:|--:|--:|--:| | Instruct (what we ship) | 0.7 | 0.80 | 20 | 0.0 | **1.5** | 1.0 | | Thinking | 1.0 | 0.95 | 20 | 0.0 | **0.0** | 1.0 | `ENABLE_THINKING=true` flips the reasoning mode **but not the sampler**. Verified by rendering the compose with the flag flipped — the emitted args ar…”
noonghunna · GitHub · Aug 16, 2026 - 19
“Scores 92.15% on LiveBench Reasoning (#2 of 58), an objective, ground-truth-scored evaluation refreshed with new questions.”
LiveBench Reasoning · Benchmark · Jun 25, 2026 - 20
“This issue is mentioned on the Opus 5.5 post [1] from Anthropic (no idea if it has been added after your rant): > Don’t ask it to show its reasoning in the reply > > What to do. Remove requests to reproduce its internal reasoning in the reply from your prompts and instructions. > > Why it matters on Opus 5.5. A request to reproduce its internal reasoning in the reply can be declined. It’s one of the flag categories. > > How. Ask Claude for what you need instead, for example, “Explain why you ch…”
dolmen · Hacker News · Sep 23, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.