Recommendation for Autocomplete
Code Completion
Our top recommendation for Code Completion, based on the public evidence we track, is Anthropic: Claude Opus 5.5.[1][2][3] Use when you need the highest-quality inline suggestions where benchmark-proven output quality outweighs cost concerns, as it tops LMArena WebDev (Elo 1818) and LiveBench Coding (89.25%). OpenAI: GPT-5.6 Sol is the next-ranked alternative. Use as a fallback when available, since LiveBench shows decent objective performance for code generation tasks.
About this recommendation
- Updated
- Sep 25, 2026
- Evidence through
- Sep 25, 2026
- Sources
- 16
- Revision
- v85
Decision audit
Why this result
Inspect the inputs and the computed order behind the recommendation.
Models screened
20
live candidates
Evaluation feeds
5
task-weighted
Winner coverage
94%
intended feed weight
Largest provider share
3 of 7
OpenAI
Sources evaluated
The task sets these weights before any model is scored.
| Evaluation feed | Weight | Winner result | Field measured |
|---|---|---|---|
| LiveBench Coding | 30% | #1 | 17/20 |
| LMArena WebDev | 25% | #1 | 17/20 |
| price weight | 20% | 97/100 | 20/20 |
| Aider Polyglot | 15% | not measured | 3/20 |
| OpenRouter usage | 10% | 87/100 | 20/20 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- OpenAI3 models
- Anthropic2 models
- xAI1 model
- Z.ai1 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 | Claude Opus 5.5Anthropic | 87 | 94% | no linked practitioner threads | #1 LiveBench Coding · #1 LMArena WebDev |
| 02 | GPT-5.6 SolOpenAI | 77 | 94% | 8 threads · 4 families · 4 cautions | #4 LiveBench Coding · #15 LMArena WebDev |
| 03 | GPT-5.6 LunaOpenAI | 75 | 94% | 8 threads · 7 families · 2 cautions | #6 LiveBench Coding · #31 LMArena WebDev |
| 04 | GPT-6 SolOpenAI | 74 | 94% | 4 threads · 3 families · 3 cautions | #5 LMArena WebDev · #9 LiveBench Coding |
| 05 | Claude Fable 5Anthropic | 71 | 94% | 2 threads · 1 families · 1 cautions | #3 LiveBench Coding · #11 LMArena WebDev |
| 06 | GLM 5.2Z.ai | 69 | 94% | 2 threads · 2 families · 0 cautions | #16 LiveBench Coding · #17 LMArena WebDev |
| 07 | Grok 4.7xAI | 69 | 94% | 2 threads · 2 families · 0 cautions | #9 LMArena WebDev · #30 LiveBench Coding |
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.
Leads both major coding benchmarks, ranking #1 on LMArena WebDev and LiveBench Coding, indicating strong preference for its code completion quality in blind human evaluations.
Best when: Use when you need the highest-quality inline suggestions where benchmark-proven output quality outweighs cost concerns, as it tops LMArena WebDev (Elo 1818) and LiveBench Coding (89.25%).
Tips
- Use when you need the highest-quality inline suggestions where benchmark-proven output quality outweighs cost concerns, as it tops LMArena WebDev (Elo 1818) and LiveBench Coding (89.25%).
Scores 83.94% on LiveBench Coding (#4) but suffers from repeated capacity failures and user reports of quality regression in newer versions.
Best when: Use as a fallback when available, since LiveBench shows decent objective performance for code generation tasks.
Tips
- Use as a fallback when available, since LiveBench shows decent objective performance for code generation tasks.
Watch out for
- Expect intermittent 503 capacity errors and 'Selected model is at capacity' failures that block completion, reported across multiple platforms and dates.
- Note user reports that GPT-6 Sol (marketed as successor) produces 'epic levels of slop,' with experienced users reverting to 5.6 Sol for quality.
Ranks #6 on LiveBench Coding (82.92%) but exhibits API compatibility issues and subscription gating mismatches that complicate deployment.
Best when: Consider for code completion where LiveBench scores indicate acceptable generation quality for mid-tier performance needs.
Tips
- Consider for code completion where LiveBench scores indicate acceptable generation quality for mid-tier performance needs.
Watch out for
- Handle API integration carefully, as the upstream rejects any `max_tokens` field presence on `/v1/responses` (even null), requiring field omission rather than standard parameter handling.
- Verify subscription tiers manually, as the model is marked `paid` in gate tables but was observed serving Free accounts, causing priority ranking mismatches.
Ranks #5 on LMArena WebDev (Elo 1686) but faces severe user quality complaints and version confusion, with reports that GPT-6 Sol is rebranded inferior material.
Best when: Consider where LMArena ranking suggests reasonable preference performance in blind evaluations.
Tips
- Consider where LMArena ranking suggests reasonable preference performance in blind evaluations.
Watch out for
- Heed widespread user reports that 'Sol 6 is a heaping pile of garbage' and 'epic levels of slop,' with the community noting it appears to be rebranded Terra-grade material rather than a true successor.
Ranks third on LiveBench Coding (85.99%) and maintains competitive placement on LMArena WebDev (#11), offering a middle ground between top-tier quality and practical deployment.
Best when: Consider for cost-conscious workflows where LiveBench shows strong objective performance (#3 of 58) without the top-tier pricing of benchmark leaders.
Tips
- Consider for cost-conscious workflows where LiveBench shows strong objective performance (#3 of 58) without the top-tier pricing of benchmark leaders.
Watch out for
- Watch for model self-identification errors where the system incorrectly denies its own model name exists, which could affect logging or debugging workflows.
Open-weight option ranking #17 on LMArena WebDev with reported cost efficiency for pair-programming workflows, though benchmark scores trail closed alternatives.
Best when: Deploy on budget-conscious inference providers like Cerebras for pair-programming workflows where you maintain active oversight, as users report delivering production work at low token costs.
Tips
- Deploy on budget-conscious inference providers like Cerebras for pair-programming workflows where you maintain active oversight, as users report delivering production work at low token costs.
Watch out for
- Accept lower benchmark standing (#17 on LMArena WebDev, Elo 1600) compared to closed-weight leaders, which may impact suggestion quality on complex completions.
Places #9 on LMArena WebDev (Elo 1632) with reported speed advantages in practice, though tooling integration gaps limit controllability.
Best when: Use for rapid iteration cycles where the 'fast option' allows quick correction loops, particularly in frontend workflows where users report it outpaces alternatives.
Tips
- Use for rapid iteration cycles where the 'fast option' allows quick correction loops, particularly in frontend workflows where users report it outpaces alternatives.
Watch out for
- Avoid if you need fine-grained thinking control via SDK, as the pi-cursor-sdk lacks mapping for Grok 4.7's thinking levels despite Cursor supporting them natively.
Frequently asked
- What is the top-ranked model for Code Completion?
- Anthropic: Claude Opus 5.5 ranks first in the current evidence-weighted comparison. Use when you need the highest-quality inline suggestions where benchmark-proven output quality outweighs cost concerns, as it tops LMArena WebDev (Elo 1818) and LiveBench Coding (89.25%).[1][2]
- What is an alternative to Anthropic: Claude Opus 5.5?
- OpenAI: GPT-5.6 Sol is the next-ranked option. Use as a fallback when available, since LiveBench shows decent objective performance for code generation tasks.[3]
Sources
- 1
“Ranks #1 of 91 on LMArena's WebDev coding arena (Elo 1818), a leaderboard built from blind human preference votes on coding tasks.”
LMArena WebDev (coding) arena · Benchmark · Sep 23, 2026 - 2
“Scores 89.25% on LiveBench Coding (#1 of 58), an objective evaluation of code generation and completion.”
LiveBench Coding · Benchmark · Jun 25, 2026 - 3
“Scores 83.94% on LiveBench Coding (#4 of 58), an objective evaluation of code generation and completion.”
LiveBench Coding · Benchmark · Jun 25, 2026 - 4
“### What version of Codex CLI is running? 0.154.0 ### What subscription do you have? ChatGPT Pro 20x ### Which model were you using? gpt-6-astra, gpt-5.6-sol, gpt-5.6-luna, gpt-5.6-terra, gpt-5.5 ### What platform is your computer? Darwin 25.6.0 arm64 arm ### What terminal emulator and version are you using (if applicable)? zsh ### Codex doctor report ### What issue are you seeing? Repeated ⚠ Selected model is at capacity. Please try a different model. Trying different model did not help. ### W…”
sunzh233 · GitHub · Sep 10, 2026 - 5
“I’m seeing the same capacity failure today (Sep 10, 2026) on **GPT-5.6 Sol** on Windows. What I observed: - A trivial prompt such as `test` / `tes` repeatedly fails with: `Selected model is at capacity. Please try a different model.` - In Codex Desktop through my local Responses-compatible router, the upstream failure is surfaced as: `503 Service Unavailable` / `Our servers are currently overloaded. Please try again later.` - I verified the local router itself is reachable (`127.0.0.1:20128`, T…”
FawziLinggo · GitHub · Sep 10, 2026 - 6
“Update: Sol 6 is a heaping pile of garbage. Just epic levels of slop. And r codex etc is full of people noticing the same. I've switched back to 5.6 Sol. What they're selling as Sol 6 is really what would have been Terra before, and it's awful.”
cmrdporcupine · Hacker News · Sep 23, 2026 - 7
“Scores 82.92% on LiveBench Coding (#6 of 58), an objective evaluation of code generation and completion.”
LiveBench Coding · Benchmark · Jun 25, 2026 - 8
“## Summary PR #330 made per-provider `max_tokens` caps land on every path. It could not help `gpt-5.6-luna` because that upstream rejects the *presence* of the `max_tokens` field on `/v1/responses` — even `null` returns HTTP 400. The only fix is to omit the field entirely. This branch adds a declarative opt-in flag (TOML field + env var) that lets the user mark specific providers as "drop the `max_tokens` field from the wire body", and threads it through `OpenCodeGoResponsesProvider`. ## What c…”
airvzxf · GitHub · Aug 10, 2026 - 9
“### Description omp's static `openai-codex` plan-requirement table does not match what the Codex backend accepted from ChatGPT **Free** accounts. I tested every `openai-codex` model omp ships, plus `gpt-5.6` from the gate table, on two Free and two Team accounts on 2026-09-25. There are two kinds of mismatch: 1. **Gated but served:** `gpt-5.6-luna` is marked `paid`, yet both Free accounts listed it and completed turns with it. omp ranks Free accounts last for this model (`planPriority = 2`) eve…”
aydgn · GitHub · Sep 25, 2026 - 10
“Ranks #5 of 91 on LMArena's WebDev coding arena (Elo 1686), a leaderboard built from blind human preference votes on coding tasks.”
LMArena WebDev (coding) arena · Benchmark · Sep 23, 2026 - 11
“Scores 85.99% on LiveBench Coding (#3 of 58), an objective evaluation of code generation and completion.”
LiveBench Coding · Benchmark · Jun 25, 2026 - 12
“+1. And there had been a long standing description for a product like this: not fit for purpose. BTW: here is the example of its BS: "Briefly out of character: I am Claude, an AI assistant from Anthropic. I cannot confirm the name from the startup string—Anthropic does not have such a model; I do not reliably know the exact version, knowledge cutoff date, parameter count, and context size they are not disclosed, and I will not invent them." This "Anthropic does not have such a model" seems to m…”
pbgcp2026 · Hacker News · Jun 12, 2026 - 13
“If you are a real engineer and uses the LLM as a pair programmer instead of delegating everything to it, even GLM 4.7 was already good enough to help you with a lot of work. I used it with Cerebras inference at a time when it had a good coding plan at a low price, and delivered tons of stuff using it.”
elzbardico · Hacker News · Jun 22, 2026 - 14
“Ranks #17 of 91 on LMArena's WebDev coding arena (Elo 1600), a leaderboard built from blind human preference votes on coding tasks.”
LMArena WebDev (coding) arena · Benchmark · Sep 23, 2026 - 15
“I'm curious: what languages or frameworks is this in? The Django code that comes out of composer2.5, to me, was insulting. Grok definitely was a step up, especially because the fast option reaaally is fast so even if it came out a bit wrong I could just whip it into perfection. For frontend work, it's a different story. You can still tell that composer2.5 is taking the long route, but I don't think it's as egregious as with Django. Also, composer2.5 would routinely run commands that were really…”
jorl17 · Hacker News · Sep 22, 2026 - 16
“## Summary On published `pi-cursor-sdk@0.3.9`, **Grok 4.7 is selectable** in `/model` and `pi --list-models`, but **pi-controllable thinking is unavailable** (`thinking=no`). Cursor already exposes a thinking control for this model; the extension does not map it. This is an extension mapping gap, not a Cursor “Grok 4.7 has no reasoning” bug. The model can still think on the Cursor side. Pi cannot drive the level with `shift+tab`, `--thinking`, or a final `:low|:medium|:high|:xhigh` suffix. ## E…”
joaomelga · GitHub · Sep 22, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.