Anthropic Claude Haiku 4.5 vs Glm 5p1
Anthropic Claude Haiku 4.5 (OpenRouter, 200,000-token context) versus Glm 5p1 (Fireworks AI, 202,800-token context). Glm 5p1 is cheaper by 3% on a blended token mix. Anthropic Claude Haiku 4.5 uniquely supports vision input and prompt caching. Glm 5p1 uniquely supports structured output (json schema). Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Anthropic Claude Haiku 4.5 vs Glm 5p1
Anthropic Claude Haiku 4.5 and Glm 5p1 are priced within 3% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.
On capability surface area, the models diverge: Anthropic Claude Haiku 4.5 supports vision input where the other does not; Anthropic Claude Haiku 4.5 supports prompt caching where the other does not; Glm 5p1 supports structured output (json schema) where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: anthropic-claude-haiku-4-5
provider: openrouter
fallback:
model: glm-5p1
provider: fireworks-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Anthropic Claude Haiku 4.5 | Glm 5p1 | |
|---|---|---|
| Input price | $1.00/M | $1.40/M |
| Output price | $5.00/M | $4.40/M |
| Context window | 200,000 | 202,800 |
| Max output | 200,000 | 131,072 |
| Function calling | ✓ | ✓ |
| Vision | ✓ | — |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | — |
| Structured output | — | ✓ |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Anthropic Claude Haiku 4.5 | Glm 5p1 | Delta |
|---|---|---|---|
| Startup 10K requests/day | $600 /mo | $684 /mo | $84.00/mo |
| Mid-market 100K requests/day | $6,000 /mo | $6,840 /mo | $840/mo |
| Enterprise 1M requests/day | $60,000 /mo | $68,400 /mo | $8,400/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your inputs include screenshots, diagrams, or product photos — Anthropic Claude Haiku 4.5 accepts image input natively, the other doesn't.
You re-send the same large system prompt across requests — Anthropic Claude Haiku 4.5 supports prompt caching, cutting input cost on repeat hits.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Anthropic Claude Haiku 4.5, switching to Glm 5p1 means re-architecting that path (and vice versa).
- • Vision input
- • Prompt caching
- • Structured output (JSON schema)
Capabilities both share (3)
- ✓ Function calling
- ✓ Streaming
- ✓ Native reasoning mode
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Max output tokens differ: 200,000 on Anthropic Claude Haiku 4.5 vs 131,072 on Glm 5p1. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Anthropic Claude Haiku 4.5 has capabilities Glm 5p1 lacks: Vision input, Prompt caching. Switching to Glm 5p1 means re-architecting any flow that depends on these.
- Glm 5p1 has capabilities Anthropic Claude Haiku 4.5 lacks: Structured output (JSON schema). Worth wiring through the agent design before commit.
- Provider changes from OpenRouter to Fireworks AI. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test Anthropic Claude Haiku 4.5 vs Glm 5p1 in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Anthropic Claude Haiku 4.5 primary, mirror 20% of traffic to Glm 5p1 in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Anthropic Claude Haiku 4.5 vs Glm 5p1
Which is cheaper, Anthropic Claude Haiku 4.5 or Glm 5p1? ▾
Glm 5p1 is cheaper by roughly 3% on a blended input + output token mix. Input prices are $1.00/M for Anthropic Claude Haiku 4.5 versus $1.40/M for Glm 5p1; output prices are $5.00/M versus $4.40/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Anthropic Claude Haiku 4.5 versus Glm 5p1? ▾
Anthropic Claude Haiku 4.5 supports up to 200,000 tokens of context. Glm 5p1 supports up to 202,800 tokens. Glm 5p1 has the larger window by a factor of 1.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Anthropic Claude Haiku 4.5 and Glm 5p1 both support tool calling? ▾
Yes — both Anthropic Claude Haiku 4.5 and Glm 5p1 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Can Anthropic Claude Haiku 4.5 and Glm 5p1 process images? ▾
Anthropic Claude Haiku 4.5 accepts native image input. Glm 5p1 does not — you would need to route image-heavy workloads through Anthropic Claude Haiku 4.5 or add a separate vision model in front of Glm 5p1.
Which model supports prompt caching for cost reduction? ▾
Anthropic Claude Haiku 4.5 supports prompt caching; the other does not. If your agent has a stable system prompt + retrieval context block that repeats across requests, Anthropic Claude Haiku 4.5 gives you a 50–90% discount on those repeated input tokens at the provider level.
When should I choose Anthropic Claude Haiku 4.5 over Glm 5p1? ▾
Your inputs include screenshots, diagrams, or product photos — Anthropic Claude Haiku 4.5 accepts image input natively, the other doesn't. You re-send the same large system prompt across requests — Anthropic Claude Haiku 4.5 supports prompt caching, cutting input cost on repeat hits.
When should I choose Glm 5p1 over Anthropic Claude Haiku 4.5? ▾
On the data this page surfaces, Glm 5p1 is the right pick when Anthropic Claude Haiku 4.5's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
How do I A/B test Anthropic Claude Haiku 4.5 against Glm 5p1 in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.