GPT-5.6 Sol vs GLM-5.3-Flash
GPT-5.6 Sol wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose GPT-5.6 Sol if you…
- Enhanced reasoning
- Strong code generation
- o-series architecture
- Best for: Reasoning-heavy tasks, Coding, Math
Choose GLM-5.3-Flash if you…
- Among the cheapest
- Fast
- Open source
- Best for: Ultra-fast, Ultra-low-cost, High concurrency
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | GPT-5.6 Sol | GLM-5.3-Flash | Verdict |
|---|---|---|---|
| Vendor | OpenAI (US) | Zhipu AI (CN) | Different vendors |
| Released | 2026.07 | 2026.08 | GLM-5.3-Flash is newer |
| Overall (rank) | 72 · #11 | 65 · #18 | GPT-5.6 Sol +7 |
| Coding | 88 · #3 | 60 · #20 | GPT-5.6 Sol +28 |
| Multimodal | 80 · #11 | 63 · #21 | GPT-5.6 Sol +17 |
| Context window | 1.05M | 128K | GPT-5.6 Sol larger |
| Max output | 128K | 64K | GLM-5.3-Flash longer |
| Effective-context | 88 | 80 | GPT-5.6 Sol more reliable |
| Input $/1M | $5 | $0.07 | GLM-5.3-Flash cheaper |
| Output $/1M | $30 | $0.25 | GLM-5.3-Flash cheaper |
| Cache discount | 50% off | none | GPT-5.6 Sol deeper |
| Speed | ~25 tok/s | ~90 tok/s | GLM-5.3-Flash faster |
| TTFT | 2.0s | 0.2s | GLM-5.3-Flash snappier |
| Function calling | 93 | 70 | GPT-5.6 Sol ahead |
| Refusal rate | ~14% | ~6% | GLM-5.3-Flash less restrictive |
| English | 92 | 65 | GPT-5.6 Sol |
| Chinese | 74 | 85 | GLM-5.3-Flash |
| Modalities | text, image | text | different coverage |
| Open weights | No | Yes | GLM-5.3-Flash is open |
| Fine-tuning | No | Yes | |
| Free tier | No free API tier | ChatGLM free | |
| SOC2 / no-train | yes / yes | no / yes | |
| Private deployment | No | Yes | GLM-5.3-Flash |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
GPT-5.6 Sol leads the overall aggregate by 7 points (72 vs 65). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GLM-5.3-Flash remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a decisive gap: GPT-5.6 Sol scores 88 against 60. On multi-file edits, SWE-style tickets and long-horizon agent loops GPT-5.6 Sol needs fewer correction turns; GLM-5.3-Flash is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
GPT-5.6 Sol leads multimodal 80 vs 63. A concrete modality difference: GPT-5.6 Sol additionally handles image. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
GLM-5.3-Flash is faster in interactive use: ~90 tok/s with 0.2s TTFT versus ~25 tok/s with 2.0s TTFT (about 3.6× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 1.05M for GPT-5.6 Sol and 128K for GLM-5.3-Flash. Effective-context scores point the same way as window size — GPT-5.6 Sol is ahead on usable recall (88 vs 80), so prefer it for long-document work where details cannot be missed.
Price & total cost
GLM-5.3-Flash is the cheaper API at $0.07/$0.25 versus GPT-5.6 Sol at $5/$30 per 1M input/output tokens — list input is about 66.7× lower. Cache discounts (GPT-5.6 Sol 50%) shift the effective bill, worked out below.
Chinese vs English
English: GPT-5.6 Sol 92 vs GLM-5.3-Flash 65. Chinese: 74 vs 85. For Chinese-language production, GLM-5.3-Flash is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.
Tool use & ecosystem
Function-calling score: GPT-5.6 Sol 93 vs GLM-5.3-Flash 70, so GPT-5.6 Sol has the edge on structured tool use. Fine-tuning is available from GLM-5.3-Flash. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | GPT-5.6 Sol | GLM-5.3-Flash | Gap |
|---|---|---|---|
| List priceno cache applied | $1,400100M in × $5 + 30M out × $30 | $15100M in × $0.07 + 30M out × $0.25 | 93.33×gap |
| With caching90% of inputs cache-hit | $1,17590M cached in × $2.5 + 10M in × $5 + 30M out × $30 | $15no published cache discount | 78.33×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.