Model comparison
GPT-5.3 Codex vs Ling 2.6 Flash
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.3 Codex #26 (Supported); Ling 2.6 Flash #154 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and Ling 2.6 Flash share 11 comparable benchmark results. 1 of 8 categories are comparable. 10 results are unique to GPT-5.3 Codex; 7 to Ling 2.6 Flash.
Updated July 21, 2026- Shared results
- 11
- GPT-5.3 Codex only
- 10
- Ling 2.6 Flash only
- 7
- Comparable categories
- 1 / 8
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.3 Codex is clearly ahead on the BenchAlign aggregate, 66.69 to 43.87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in coding, where it averages 67.2 against 27.
GPT-5.3 Codex is the reasoning model in the pair, while Ling 2.6 Flash is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.3 Codex gives you the larger context window at 400K, compared with 262K for Ling 2.6 Flash.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GPT-5.3 Codex | Δ | Ling 2.6 Flash |
|---|---|---|---|
| Coding | GPT-5.3 Codex67.2 | Margin← 40.2 | Ling 2.6 Flash27.0 |
| Agentic | GPT-5.3 Codex71.4 | MarginNo overlap | Ling 2.6 FlashNot measured |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | Ling 2.6 Flash59.0 |
| Inst. Following | GPT-5.3 CodexNot measured | MarginNo overlap | Ling 2.6 Flash57.0 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.3 Codex | Ling 2.6 Flash | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | Ling 2.6 FlashNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | Ling 2.6 Flash209.5 tok/s | Ling 2.6 Flash has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | Ling 2.6 Flash1.07 s | Ling 2.6 Flash reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | Ling 2.6 Flash262K | GPT-5.3 Codex lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GPT-5.3 Codex | Ling 2.6 Flash | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 77.3% | — | Not comparable |
| OSWorld-VerifiedSource | 64.7% | — | Not comparable |
| τ²-bench resultsSource | 86% | 86% | Tie |
| Gert LabsSource | 57.47% | — | Not comparable |
| JobBenchSource | 33.7% | — | Not comparable |
| GDPval-AASource | — | 2.2% | Not comparable |
| GDPval-AASource | — | 545 | Not comparable |
| AA Agentic IndexSource | — | 2.3% | Not comparable |
CodingGPT-5.3 Codex wins7 benchmarks
| Benchmark | GPT-5.3 Codex | Ling 2.6 Flash | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | — | Not comparable |
| SWE-bench ProSource | 56.8% | — | Not comparable |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | — | Not comparable |
| AA-SciCodeSource | 53.2% | 27.1% | GPT-5.3 Codex leads |
| SciCodeSource | — | 27% | Not comparable |
| AA Coding IndexSource | — | 25.3% | Not comparable |
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | GPT-5.3 Codex | Ling 2.6 Flash | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | 14.1% | GPT-5.3 Codex leads |
| AA-GPQA DiamondSource | 91.5% | 59.3% | GPT-5.3 Codex leads |
| AA-HLESource | 39.9% | 6.2% | GPT-5.3 Codex leads |
| AA-Omniscience IndexSource | 9.9% | -65.7% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 51.8% | 15.4% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 86.9% | 95.8% | GPT-5.3 Codex leads |
| GPQASource | — | 59% | Not comparable |
Multimodal2 benchmarks
Frequently Asked Questions (2)
Which is better, GPT-5.3 Codex or Ling 2.6 Flash?
GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 66.69 to 43.87.
Which is better for coding, GPT-5.3 Codex or Ling 2.6 Flash?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 27. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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