GLM-5 vs Llama 3 8B Instruct
GLM-5 (2026) and Llama 3 8B Instruct (2024) are frontier reasoning models from Zhipu AI and AI at Meta. GLM-5 ships a 200k-token context window, while Llama 3 8B Instruct ships a 8k-token context window. On MMLU PRO, GLM-5 leads by 45.5 pts. On pricing, Llama 3 8B Instruct costs $0.03/1M input tokens versus $0.60/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3 8B Instruct is ~1900% cheaper at $0.03/1M; pay for GLM-5 only for reasoning depth.
Decision scorecard
Local evidence first| Signal | GLM-5 | Llama 3 8B Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, Classification, and JSON / Tool use |
| Context window | 200k | 8k |
| Cheapest output | $2.08/1M tokens | $0.04/1M tokens |
| Provider routes | 7 tracked | 17 tracked |
| Shared benchmarks | MMLU PRO leader | 2 rows |
Decision tradeoffs
- GLM-5 holds a shared-benchmark lead on MMLU PRO, ahead by 45.5 points.
- GLM-5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
- Llama 3 8B Instruct has the lower cheapest tracked output price at $0.04/1M tokens.
- Llama 3 8B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3 8B Instruct for Coding, Classification, and JSON / Tool use.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5
$1,000
Cheapest tracked route/tier: OpenRouter
Llama 3 8B Instruct
$34.00
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $966. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, GCP Vertex AI, and NVIDIA NIM; start route-level A/B tests there.
- Llama 3 8B Instruct is $2.04/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Provider overlap exists on Fireworks AI, OpenRouter, and Together AI; start route-level A/B tests there.
- GLM-5 is $2.04/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GLM-5 adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-11 | 2024-04-18 |
| Context window | 200k | 8k |
| Parameters | 744B total, 40B active | 8B |
| Architecture | mixture of experts | decoder only |
| License | MIT(OSI) | Llama 3 Community |
| Openness | Open source | Open weights |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | 2025-11 | 2023-03 |
Pricing and availability
| Pricing attribute | GLM-5 | Llama 3 8B Instruct |
|---|---|---|
| Input price | $0.60/1M tokens | $0.03/1M tokens |
| Output price | $2.08/1M tokens | $0.04/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5 | Llama 3 8B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GLM-5 | Llama 3 8B Instruct |
|---|---|---|
| MMLU PRO | 86.0 | 40.5 |
| Google-Proof Q&A | 86.0 | 44.8 |
Deep dive
On shared benchmark coverage, MMLU PRO has GLM-5 at 86 and Llama 3 8B Instruct at 40.5, with GLM-5 ahead by 45.5 points; Google-Proof Q&A has GLM-5 at 86 and Llama 3 8B Instruct at 44.8, with GLM-5 ahead by 41.2 points. The largest visible gap is 45.5 points on MMLU PRO, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: GLM-5, function calling: GLM-5, and tool use: GLM-5. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, GLM-5 lists $0.60/1M input and $2.08/1M output tokens on the cheapest tracked provider, while Llama 3 8B Instruct lists $0.03/1M input and $0.04/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 8B Instruct lower by about $1.01 per million blended tokens. Availability is 7 providers versus 17, so concentration risk also matters.
Choose GLM-5 when reasoning depth and larger context windows are central to the workload. Choose Llama 3 8B Instruct when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, GLM-5 or Llama 3 8B Instruct?
GLM-5 supports 200k tokens, while Llama 3 8B Instruct supports 8k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, GLM-5 or Llama 3 8B Instruct?
Llama 3 8B Instruct is cheaper on tracked token pricing. GLM-5 costs $0.60/1M input and $2.08/1M output tokens. Llama 3 8B Instruct costs $0.03/1M input and $0.04/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 or Llama 3 8B Instruct open source?
GLM-5 is listed under MIT. Llama 3 8B Instruct is listed under Llama 3 Community. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, GLM-5 or Llama 3 8B Instruct?
GLM-5 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, GLM-5 or Llama 3 8B Instruct?
GLM-5 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GLM-5 and Llama 3 8B Instruct?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Llama 3 8B Instruct is available on AWS Bedrock, DeepInfra, OctoAI API (Deprecated), Fireworks AI, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.