GLM 5.2 vs GPT-5.6 Sol: Benchmarks, Pricing, and the 6.8× Cost Gap

GPT-5.6 Sol launched on July 9, 2026 as OpenAI's new flagship model — the top tier in a three-model family (Luna, Terra, Sol) and the successor to GPT-5.5. On the Artificial Analysis Intelligence Index, Sol scores 59 versus GLM 5.2's 51. On GPQA Diamond, Sol reaches 94.6% versus GLM 5.2's 89%. These are real gains.

But output tokens cost $30 per million with Sol. GLM 5.2 charges $4.40. That is a 6.8× price difference on the dimension that compounds most at scale.

On SWE-bench Pro — real GitHub issue resolution — Sol scores 64.6% and GLM 5.2 scores 62.1%. A 2.5-point gap, at 6.8× the cost. Both models carry approximately 1 million token context windows. Both are fast enough for production workloads, though Sol's speed varies significantly by provider.

Choose GLM 5.2 if your workload is text-only, cost scales with volume, and SWE-bench-level coding performance (62.1%) is sufficient — you get nearly identical software engineering results at a fraction of the price.

Choose GPT-5.6 Sol if you need vision input, maximum reasoning depth on science and math (GPQA 94.6%), or the strongest available Terminal-Bench agentic performance (88.8%) and budget is not the primary constraint.

Quick Comparison

Benchmark Performance

GPT-5.6 Sol holds a meaningful lead on every published benchmark, but the gap varies significantly by task type.

The SWE-bench Pro result is the most important number for teams evaluating these models for software engineering use cases. Sol scores 64.6% versus GLM 5.2's 62.1% — a 2.5-point gap on real GitHub issue resolution across hundreds of repositories. For most engineering workloads, that margin is not perceptible in production.

The GPQA Diamond gap is more significant: Sol at 94.6% versus GLM 5.2 at 89% — 5.6 points on doctoral-level reasoning in physics, chemistry, and biology. If your workload involves deep scientific analysis, literature review, or complex research tasks, Sol's reasoning advantage compounds.

Terminal-Bench shows the largest gap: Sol at 88.8% versus GLM 5.2 at 78%, a 10.8-point lead on agentic terminal tasks (shell commands, file navigation, multi-step scripting). Sol's Ultra mode reaches 91.9% — the highest published Terminal-Bench score as of July 2026, above Claude Mythos 5 (88.0%) and GPT-5.5 (88.0%).

For context: Sol's SWE-bench Pro of 64.6% is notably lower than Claude Fable 5's 80.0% on the same benchmark. Sol leads on agent metrics and science reasoning; it does not lead on software engineering.

Pricing Breakdown

This is the central difference between the two models.

The 85% output token discount is the number that matters at production scale. Most API workloads generate more output tokens than input tokens, and output costs dominate monthly bills at any meaningful volume.

Concrete example: A coding assistant that processes 50K input tokens and generates 30K output tokens per request costs $0.202 with GLM 5.2 and $1.150 with GPT-5.6 Sol — 5.7× more per call. At 5,000 daily requests, that is an annualized cost difference of approximately $1.7 million.

GPT-5.6 also comes in lower tiers: Terra at $2.50/$15 per million and Luna at $1.00/$6.00 per million. Luna's output price of $6.00/M is still 36% more expensive than GLM 5.2 at $4.40/M, but Luna offers less capability than Sol.

If cost is your binding constraint and you want to stay within the GPT-5.6 family, Terra ($2.50/$15) and Luna ($1/$6) are worth evaluating. Neither matches GLM 5.2's price-to-performance on SWE-bench, but Luna is the closest.

Speed and Latency

GLM 5.2 generates text at a consistent 158 tokens per second across standard API providers. GPT-5.6 Sol's throughput is more variable.

On Cerebras hardware, Sol reaches 750 tokens per second — one of the highest published inference speeds for a frontier model. On standard OpenAI API infrastructure, Artificial Analysis characterizes Sol as notably slow. The Cerebras speed is real but requires specific infrastructure access; most API users will see significantly lower throughput.

For production systems where response latency matters — IDE assistants, real-time coding copilots, streaming chat interfaces — GLM 5.2's predictable 158 t/s on standard providers is an advantage over Sol's variable performance unless you have direct Cerebras access.

Context Window

Both models support approximately 1 million tokens in a single request.

Sol's maximum output of 128,000 tokens is a significant advantage for tasks that generate long responses — full codebases, comprehensive reports, extended analyses. GLM 5.2's output limit is lower, which matters for generation-heavy workloads where a single response needs to be very long.

For input-heavy tasks — feeding large codebases, long documents, or extended conversation histories — both models are effectively equivalent. The 2,000-token difference in context window (1.05M vs 1.048M) is negligible in practice.

Vision Capabilities

GPT-5.6 Sol accepts image input alongside text. GLM 5.2 is text-only.

Sol's vision capabilities enable:

• Screenshot-to-code generation — pass a UI screenshot, receive working code
• Document processing — PDFs with embedded images, charts, diagrams
• Image analysis and description — visual QA, object identification, chart interpretation
• UI mockup understanding — design-to-implementation workflows

For any workflow that requires image input, Sol is the only option of the two. GLM 5.2's advantages in cost and speed are irrelevant when the task requires visual understanding.

For text-only workloads — the majority of API use cases including coding, analysis, writing, and agentic tasks — GLM 5.2's lack of vision is not a limitation.

Open Source vs Closed

GLM 5.2 weights are MIT-licensed and available on Hugging Face. GPT-5.6 Sol is closed source with no self-hosting option.

Practical implications:

• Self-hosting: GLM 5.2 can run on your own GPU infrastructure, eliminating per-token costs at scale. Sol requires OpenAI's API.
• Fine-tuning: GLM 5.2 allows weight-level fine-tuning on private data with no obligation to share results. Sol's fine-tuning (if offered) goes through OpenAI's API.
• Air-gapped deployment: GLM 5.2 can run fully offline. Sol cannot.
• Regulatory compliance: For deployments requiring data residency guarantees, self-hosted GLM 5.2 offers stronger isolation.

The access restrictions on GPT-5.6 Sol's launch — initially limited to a small group of trusted partners under government-related constraints before general release on July 9 — are a reminder that API-only models carry availability risk that open-weight models do not.

API Integration

Both models use the OpenAI chat completions format, making migration straightforward.

# GLM 5.2 via Z.ai
from openai import OpenAI
client = OpenAI(
    api_key="your_zai_key",
    base_url="https://api.z.ai/v1"
)
response = client.chat.completions.create(
    model="glm-5.2",
    messages=[{"role": "user", "content": "Explain MoE architectures."}]
)

# GPT-5.6 Sol via OpenAI
client = OpenAI(api_key="your_openai_key")
response = client.chat.completions.create(
    model="gpt-5.6-sol",
    messages=[{"role": "user", "content": "Explain MoE architectures."}]
)

Switching between the two requires only changing api_key, base_url, and model. Text-based integrations are immediately compatible. Applications that pass image inputs to Sol will need to handle those separately when using GLM 5.2.

When to Choose GLM 5.2

GLM 5.2 is the better choice when:

• Your workload is text-only — no image, audio, or video input required
• Output cost compounds at scale: 85% savings at production volume is significant
• You need predictable speed: 158 t/s reliably, without Cerebras infrastructure
• SWE-bench Pro performance at 62.1% is sufficient for your coding use cases (Sol adds only 2.5 points at 6.8× the cost)
• You need MIT-licensed open weights for fine-tuning, self-hosting, or air-gapped deployment
• You're comparing the two models' reasoning and want to start with the cheaper option

When to Choose GPT-5.6 Sol

GPT-5.6 Sol is the better choice when:

• Your application requires image input — Sol's vision capabilities are non-negotiable for this
• Deep scientific reasoning matters: Sol's GPQA Diamond of 94.6% versus GLM 5.2's 89% is meaningful for research-grade tasks
• You need maximum Terminal-Bench agentic performance (88.8% vs 78%) for complex shell and CLI automation
• You need 128K output tokens in a single response — Sol's output window is significantly larger
• You have Cerebras infrastructure access and need 750 t/s throughput
• API reliability and OpenAI's enterprise SLAs are organizational requirements

Frequently Asked Questions

Is GPT-5.6 Sol better than GLM 5.2?

On benchmark scores, yes — Sol leads on every published metric: AA Index (59 vs 51), GPQA Diamond (94.6% vs 89%), Terminal-Bench (88.8% vs 78%), and SWE-bench Pro (64.6% vs 62.1%). The SWE-bench Pro gap is the smallest at 2.5 points. Whether "better" justifies 6.8× higher output cost depends entirely on your workload and budget.

How much more expensive is GPT-5.6 Sol than GLM 5.2?

Sol's output tokens cost $30/M versus GLM 5.2's $4.40/M — 85% more expensive, or 6.8× the price. Input tokens cost $5/M versus $1.40/M — 72% more expensive. At scale, this difference is substantial: a team spending $10K/month on GLM 5.2 would spend approximately $68K/month for equivalent volume with Sol.

Does GLM 5.2 support vision like GPT-5.6 Sol?

No. GLM 5.2 is a text-only model. GPT-5.6 Sol accepts image input alongside text, enabling screenshot-to-code, document analysis with embedded images, and visual QA. See Is GLM 5.2 Multimodal? for details on GLM 5.2's modality support.

What is the context window of GPT-5.6 Sol vs GLM 5.2?

Sol supports approximately 1,050,000 tokens; GLM 5.2 supports 1,048,576 tokens. The input context window is effectively identical — both handle approximately 1 million tokens. Sol's advantage is on the output side: up to 128,000 tokens per response versus GLM 5.2's lower output limit. For long-generation tasks, Sol's output window is meaningfully larger.

Is GPT-5.6 Sol fast?

It depends on infrastructure. On Cerebras hardware, Sol reaches 750 tokens per second — among the fastest published speeds for any frontier model. On standard OpenAI API infrastructure, Artificial Analysis rates Sol as notably slow. If speed is a requirement, GLM 5.2's consistent 158 t/s on standard providers is more predictable unless you have Cerebras access.

Can I switch from GPT-5.6 Sol to GLM 5.2 without rewriting my code?

For text-based applications, yes — change api_key, base_url (to https://api.z.ai/v1), and model (to glm-5.2). Both use the OpenAI chat completions format. Applications that send image content in messages will need to handle those modalities separately since GLM 5.2 is text-only.

Is GLM 5.2 open source?

Yes. GLM 5.2 weights are available on Hugging Face under the MIT license, allowing commercial use, fine-tuning, and redistribution with no restrictions beyond preserving the copyright notice. GPT-5.6 Sol is closed source with no self-hosting option.

Bottom Line

GPT-5.6 Sol is a stronger model by every benchmark — and it costs 6.8× more per output token. The benchmark gaps range from meaningful (GPQA Diamond: 5.6 points, Terminal-Bench: 10.8 points) to narrow (SWE-bench Pro: 2.5 points). Sol also adds vision input and a larger output window.

For the majority of text-only, cost-sensitive production workloads — especially coding, analysis, and agentic tasks where SWE-bench-level performance is the bar — GLM 5.2 delivers comparable results at a fraction of the price. Sol's premium is justified when you specifically need vision capabilities, maximum science reasoning depth, or agentic shell performance that no open-weight model currently matches.

Try GLM 5.2 — no API key needed: glm5.app/chat.

Sources

• GPT-5.6 Sol (max) — Intelligence, Performance & Price Analysis — Artificial Analysis (AA Index 59, speed, pricing)
• OpenAI launches its new family of models with GPT-5.6 — TechCrunch (release date, model family)
• GPT-5.6 Sol: Benchmarks, Pricing & API Access Guide 2026 — EdenAI (GPQA 94.6%, SWE-bench 64.6%, Terminal-Bench 88.8%)
• GPT-5.6 Sol — API Pricing & Benchmarks — OpenRouter (pricing $5/$30/M, context 1.05M)
• GLM-5.2 (max) — Intelligence, Performance & Price Analysis — Artificial Analysis (GLM 5.2 benchmarks, speed 158 t/s, pricing $1.40/$4.40)